Kamal Beydoun

Violeta Felea

Wireless Sensor Networks Routing over Zones

Keywords: wireless sensor networks, hierarchical routing, zone architecture

In this paper, we propose a routing protocol for wireless sensor networks based on a two-level, zone-based architecture. DV is applied in both intra-zone and inter-zone routing, based on the hop metric. Our solution is original because it is instrumentation-free (sensors are both localization and energy unaware) and completely distributed. We show performances of the proposed algorithm evaluating the overhead generated by the construction of the infrastructure needed in routing. Simulations for MICA2 sensors have given us indications on the energy consumption -almost 4*10 -4 % of the total battery capacity, on the scalability property of the algorithm and on the memory size of the data structure used for routing -almost 13% of the RAM memory. Moreover, memory constraints allow us to determine a lower bound for the number of zones.

I. INTRODUCTION

Wireless sensor networks (WSNs) constitute one of the recent technologies which are used to monitor and analyze largescale, real-world phenomena, a bridge between the physical world and the information technology's need of situational awareness. One major requirement of WSNs is information transmission: sensors communicate useful environmental data to one or more resourced base stations (BS or sinks). The simplest WSN topology allowing this communication is the single-hop star, feasible when all sensor nodes can communicate directly with the sink. However, when deployed over large areas, nodes are generally distant from the base stations, therefore single-hop star architectures become limited. In multi-hop networks, one critical point is defining an appropriate route for each message intended for the base station. Moreover, large-scale deployments are frequently used in sensor networks and virtual architectures need to be designed. This challenge justifies our contribution on hierarchical routing approaches for communications in wireless sensor networks. The network architecture on which our routing protocol is based is constructed on top of zones. The detailed algorithm and its evaluation are given in [START_REF] Beydoun | Wireless Sensor Network Infrastructure: Construction and Evaluation[END_REF]. In section 3, we remind only the main idea. Compared to other organizational techniques -see section 2 -like clustering, generally assuming network instrumentation, our solution is instrumentation-free (sensors are both location and energy unaware) and completely distributed. Our proposal of a two-level routing infrastructure over this virtual architecture is presented in section 4. Wireless sensor networks are submitted to particular constraints of energy, and size of the storage space; moreover WSN applications generally need large-scale deployments. Considering these constraints, we evaluate the construction of our routing data infrastructure in section 5. The last section concludes our work.

II. ROUTING CHALLENGES IN WIRELESS SENSOR

NETWORKS

One general solution that responds to the large scale deployment of WSNs is network structuring. The most frequently employed approach is clustering.

Clustering has been inspired from the cluster networks in which particular nodes, called cluster heads, have additional functionality of managing cluster formation and maintenance. In sensor networks, these functionalities are associated to more powerful nodes, like in LEACH [START_REF] Heinzelman | Energy-efficient communication protocol for wireless sensor networks[END_REF], APTEEN [START_REF] Manjeshwar | APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks[END_REF], CMLDA [START_REF] Dasgupta | An efficient clustering-based heuristic for data gathering and aggregation in sensor networks[END_REF], Hierarchical Clustering [START_REF] Bandyopadhyay | An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks[END_REF], and HEED [START_REF] Younis | Heed: A hybrid, energy-eicient, distributed clustering approach for ad hoc sensor networks[END_REF]. All clustering approaches assume that cluster heads can communicate directly (in one hop) with other cluster-heads, or possibly directly with the base station. We may infer that the underlying networks are heterogeneous, some nodes having more (or at least adaptive) transmission power and more energy. Moreover, if energy-balancing techniques are used, like cluster rotation, this requires that all network nodes should potentially be able to reach cluster heads or even the BS in one hop. Routing is out of question in this architecture because of the strong hypothesis on node accessibility. Some clustering approaches are hierarchical (Hierarchical Clustering), cluster heads forming a hierarchy for packet communication. Once again, in this approach, the cluster heads are supposed to be able to communicate directly and no communication protocol is given to explain the way in which this hierarchy is exploited. The challenge in cluster formation is defining the appropriate number of cluster heads and their distribution: few cluster heads generate important cluster management overhead, whereas geographically close cluster heads do not allow good coverage of the network. Some distributed approaches forming clusters exist (HEED, Hierarchical Clustering, LEACH), but they are tributary to the distributed aspect of the algorithm and can not assure good properties for the cluster head number and their distribution. C-LEACH [START_REF] Heinzelman | An Application-Specific Protocol Architecture for Wireless Microsensor Networks[END_REF] and C 2 E 2 S [8] -combined solutions between cluster and chain strategies -are centralized approaches which make use of the BS to produce better organization, based on node energy. More generally, centralized approaches can not be applied to WSN systems, because of the important overhead generated by the construction of a global network view at each sensor node.

When geographical node position is required to form groups of sensors, two other classes of architectural approaches can be identified: the grids or the zones (HPAR [START_REF] Li | Hierarchical Power-aware Routing in Sensor Networks[END_REF], VGA [START_REF] Al-Karaki | Data Aggregation in Wireless Sensor Networks -Exact and Approximate Algorithms[END_REF], TTDD [START_REF] Luo | TTDD: two-tier data dissemination in large-scale wireless sensor networks[END_REF]). In TTDD, nodes are grouped in zones and dissemination nodes collect data from source nodes and send it to the sink. Node geo-localization may be an expensive mechanism for largely-deployed networks. The GPS may be partially used (only beacon nodes are location-aware), but geo-localization strategies may generate important computation overhead and error. Moreover, GPS solutions are not available in any type of environment.

All previously cited works tend to find architectural solutions in order to maintain a structured view of networks, enabling easy communication exchange between nodes in transmitting the sensed information to the BS. Every proposed solution is either based on particular hypothesis (geolocalization of node, transmission power control, direct BS access) or makes use of centralized algorithms needing permanent energy information over nodes or links. We assume these approaches difficult to implement and to embed in real wireless sensor networks, characterized by a high level of decentralization, low resources and small storage capacity. These arguments justify our routing protocol for WSNs. We make no assumptions concerning node localization, adaptive transmission power, or energy level awareness. Our approach is completely distributed, based on a neighboring discovery protocol and virtual zone construction.

III. VIRTUAL NETWORK ARCHITECTURE

Routing in largely-deployed wireless sensor networks needs network structuring, in order to avoid energy waste when flooding the network with packets addressed to particular targets: the base stations or other well-identified sensor nodes. We propose an inexpensive algorithm which forms disjoint zones using the basic metric: the number of hops. We do not intend to make this construction complex, using energy metrics (of nodes or links) because this kind of information is highly dynamic and its update would generate important control overhead. The aim of our structuring is providing

• local views for routing inside zones, because only the nodes in the proximity of the sensor containing the data to be sent may be involved in the packet's routing and • global view over all zones, because only the zones in the direction of the target's zone are concerned in the routing. Our hypotheses are the following: nodes are randomly deployed, localization unaware, energy unaware, bidirectional communication links are assumed, no transmission power variation is available. Each node has a unique identification assigned.

The zone partitioning algorithm takes as parameters the zone radius (measured in number of hops R) and the number of zones (nZ). The detailed algorithm is given in [START_REF] Beydoun | Wireless Sensor Network Infrastructure: Construction and Evaluation[END_REF], here we briefly describe the principle. nZ nodes are chosen as inviting nodes, which have the initiative of propagating the zone invitation packets. Concurrent invitation packets are dealt with at the receiver using different packet types: INVITATION, DISAGREEMENT. Inviting nodes do not have another role besides initiating the zone construction; this is the reason why our algorithm can not be classified as clustering method. Moreover, it does not use any localization information on nodes, so it can not be considered grid or zone structuring according to their definition given in the previous section. The best choice of R and nZ is a difficult problem: the network should be properly sectioned and every node should join a zone. Even though we do not address this matter here, a lower bound for the number of zones is determined, based on the memory metric.

Our zone partitioning approach defines two types of nodes: border (at the frontier with another zone) and normal. Every node (normal or border) has an internal routing table (of its zone) while every border node constructs a border-table with information necessary for the inter-zone routing phase. Consequently, one entry in this border-table (BorderT) contains the following information:

• the identification of the neighboring zone (neighZoneId),

• the list of all border nodes in each of its neighboring zones (borderNodeIds).

IV. ZONE ROUTING

Our routing approach is inspired from the adhoc networks: no predefined infrastructure exists. Proactive solutions seem to us adaptable to the aimed WSN applications, in which little sensor node mobility is involved -the sink mobility is a more frequent case, which is not addressed here. We use the hop metric, which is stable throughout the lifetime of the network: updates need to be made only when new sensor nodes join the network or when existing nodes leave it.

Our routing protocol is table-based and zone-based, the table construction being done in two phases, presented next: the intra-zone routing table construction and the inter-zone routing table construction.

A. The intra-zone routing table

The intra-zone routing table is constructed based on the DV algorithm (Bellman-Ford).

Two well-known table-driven algorithms are used for adhoc routing, Distance-Vector (DV) and Link-State (LS). Distance vector protocols use distance calculation plus an outgoing network interface (a vector) to choose the best path to a network destination. Link-State protocols track the status and connection type of each link and produce a calculated metric based on these, which help to choose the best path. We chose DV because it needs less computation resources and smaller storage space than LS. Distance vector routing protocols are efficient for small environments; on the other hand, link state convergence occurs faster than distance vector convergence.

An entry in this intra-zone routing table, denoted IntraZoneRT, contains the following information:

• the destination node (destNodeId),

• the next hop (nextHopId),

• the metric (M) -computed in number of hops,

• the type of the destination node, normal or border (nodeType), • moreover, if the destination node is a border node, the entry contains the list of neighboring zones (neighZoneIds). This construction exploits the principle of DV algorithm on a smaller number of nodes (those of a zone). The maximum hop count method (also used in the RIP protocol) has been used in order to avoid routing loops.

B. The inter-zone routing table

The principle of the DV algorithm is applied between zones to form the inter-zone routing tables. These are necessary for the border nodes which are responsible to relay packets between zones. In order to avoid redundant computation and thus flooding border nodes with similar information, a particular border node is chosen per zone. It is called chief node -its type is identified by BORDER-CHIEF. It has the highest identification between the border node identifications in the zone; this information can be extracted from the IntraZoneRT routing table. The chief node computes the interzone routing table and forwards it to all the other border nodes of the zone. The choice of the chief node may lead to asymmetric energy consumption, which may be adjusted by natural rotation solutions.

One entry in the inter-zone routing table, denoted InterZoneRT, contains the destination zone (destZoneId), the next zone (nextZoneId) and the zone metric (zoneM).

The metric used in the InterZoneRT construction is the zone metric. It evaluates the cost of crossing a zone and is defined as the average length (in number of hops) of the longest paths between any two nodes of the zone. The zone metric is computed during the IntraZoneRT construction.

The packets exchanged in this table-construction phase contain the following information: the source node (srcId), the next hop (nextHopId), the zone of the source node (srcZoneId), the subject (which may be COMPL When receiving a routing control packet packet, a node processes it depending on its type (NORMAL, BORDER or BORDER-CHIEF). A normal node forwards the packet towards the final destination; a chief node updates the InterZoneRT routing table and sends updates to some border nodes of its zone and to the border nodes of the neighboring zones. Finally, a border node forwards the packet to the chief node of its zone, if the packet comes from a border node of a neighboring zone. Otherwise, if the border node is a final destination, either it updates the table, or it forwards the packet to the border nodes of the neighboring zones; else, it sends the packet to the final destination.

V. ROUTING EVALUATION

The J-Sim simulator1 [START_REF] Sobeih | A simulation and emulation environment for wireless sensor networks[END_REF] has been our choice for the routing protocol experimentation in sensor networks. Built upon the concept of autonomous component programming model, it allows defining customized sensor environments (sensor's layers and the communication infrastructure). Among other several network simulation platforms -Tossim [START_REF] Levis | TOSSIM: A Simulator for TinyOS Networks[END_REF], NS-2 [START_REF]The Network Simulator NS-2[END_REF] (adapted for WSNs) -J-Sim looked the most appropriate for our experiments, because it allows large-scale simulations and offers the possibility of integrating one's own routing protocol. J-Sim is developed entirely in Java and uses Tcl as script language to facilitate scenario setups.

The simulations were performed on a large area of 1500m over 1500m. The node's maximum transmission range was set to 150m. The network size was varied between 400, 500 and 600 nodes.

In order to evaluate the data infrastruction construction needed for the routing protocol, we considered four metrics: the overhead, the energy consumption, the scalability and the memory capacity. Each of these measures is influenced by both the zone construction and the routing table construction. We present results separately for each phase. One particular evaluation metric is the generated overhead. We estimate it by both the number of sent and received packets and we infer the energy consumption, on the basis of the energy model of the MICA2 sensors [START_REF]Crossbow. MICA2 Data sheet[END_REF] -radio reception drains 10 mAh, radio transmission drains 27 mAh while data transmission rate is 38400 bits/s and the initial battery energy is 2900 mAh. In this energy evaluation, we neglected the computation cost, generally very small compared to the transmission/reception cost (for MICA2, 8mAh are drained by CPU computation).

A. Overhead

We define the overhead as the cost in terms of number of control messages exchanged during both zone and routing table construction.

1) Zone construction overhead: The overhead in zone construction depends on the number of zones (nZ), and is independent of the zone radius R, once the error ratio (the number of unassigned nodes) is close to zero. The average number of sent messages per node varies from 3 to 5,5 when the number of zones varies from 5 to 25. The average number of received messages per node varies from 16 to 19. These results and more comments on the zone construction evaluation are given in [START_REF] Beydoun | Wireless Sensor Network Infrastructure: Construction and Evaluation[END_REF].

2) Routing overhead: The most costly operation in the routing algorithm is the routing table construction. Figures 1 and2 show the average number of sent and received packets per node, respectively, for the same 500-node network. The variation of the zone radius has the same effect as previously: when the error ratio is close to zero, no more overhead is produced when increasing the zone radius.

B. Energy consumption and scalability

We also estimated the energy consumption during the two phases, zone and table construction when varying the size of the network, from 400 to 600, for a fixed zone radius, R=15, and for the number of zones (nZ) varying between 10, 15 and 20. At the same time we evaluated the scalability of the algorithm: no major degradation in performances should be identified, even when a large number of sensor nodes is involved.

Figure 3 shows the average percentage of the battery consumption per node during the setup phase of the protocol (covering both zone and routing tables construction). The energy model of the MICA2 sensors cited previously is applied. These computations depend on both the number of received and sent packets. We can note little variation in the number of sent packets when the number of nodes is increased (40 to 49 for 15 zones, and 45 to 51 for 20 zones). This fluctuation is slightly more important (in percentage) for the number of received packets. More packets are received than sent, because of the radio communication model used in wireless networks (one sent packet may be received by several active nodes in the source node's vicinity). The total energy spent in communications varies sublinearly with the number of nodes which gives good scalability property to the algorithm.

C. Memory capacity

One other important evaluation metric for the WSN routing protocol that we propose is the size of the data structure used for routing. Using table-driven algorithms may need important memory space in the context of largely-deployed networks. We reduce already this complexity by the two-level routing tables that we construct. Next, we are interested in the space complexity, in terms of number of bytes occupied by the involved data structures. As far as we know, no other routing mechanism proposed in literature for wireless sensor networks considers this metric.

The formula computing the size (in bytes) for the routing data structures is given in Table I, for N deployed nodes, when nZ zones are constructed, each zone having in average nB border nodes.

Zone

Routing We experimentally evaluated the size needed for the data structures used by the routing algorithm (for a 600-node network, nZ=15, R=15). These experimental results confirm the estimation given in the general case. An average of 4Kb per node would be necessary to save data information for routing, which represents almost 13% of the RAM memory of a standard Mica2 sensor node.

D. Lower bound for the number of zones

The previous metric does not only estimate the size of the needed data structure in order to assure pro-active routing based on routing tables; it also gives a lower bound of the number of zones. This computation is based on a memory limit imposed for sensors in respect with the total memory capacity of a sensor. Depending on technologies, this total capacity may vary, therefore we make the following assumption: nodes technically dispose of MEM_RAM RAM memory capacity. Obviously, only a fraction of the total available memory can be used for protocol data structures. We denote it by the MAX_MEM_PRCTG percentage (%). Considering the theoretical memory capacity needed by the protocol for a normal node, we have

E. Comparison with other hierarchical approaches

Similar works proposing routing over particular sensor network structures exist as cited previously. Direct and objective comparison of results is difficult to assess for several reasons:

• different hypotheses are made -network instrumentation (through sensor positioning systems), network heterogeneity (concerning communication range), • simulators are not the same -LEACH and APTEEN use an extension of the NS-2 simulator which was not initially designed for sensor networks (other works do not cite their implementation tool). Our choice was the J-Sim simulator, designed explicitly for sensor networks. While imposing standard implementation tools is still possible, in spite of the cost of code migration, the differences in hypotheses is a serious problem which prevents us from real comparison.

However, remarks can be made concerning general trends in our solution, compared to similar existing works:

• our solution is neighboring-based (completely distributed) opposed to centralized ones (like some graph approaches), • our solution to maximize the network lifetime is based on the number of radio hops (no communication overhead is involved), while several existing works are based on energy (involving information exchange), • our virtual network architecture, zone-based, is not costly compared to cluster organization which implies maintenance overhead (to avoid cluster head exhaustion). There are points of similarities in the strategy of our routing protocol over zones and the ZRP [START_REF] Pearlman | Determining the Optimal Configuration of the Zone Routing Protocol[END_REF] protocol. The latter is proposed for ad-hoc networks, which prevented us from including it into the related work. However, it presents some similarities with our approach and we present here the differences. In ZRP, each node maintains its own routing zone, defined by the nodes whose minimum distance in hops from the node in question is no greater than a parameter referred to as zone radius. This implies that the routing zones of neighboring nodes overlap. This is not the case in our approach, zones being completely disjoint. ZRP proactively maintains routing information for a local neighborhood, while reactively acquiring routes to destinations beyond the routing zone. We use proactive approaches, for both inter and intra zone routing.

VI. CONCLUSION AND PERSPECTIVES

Energy consumption and data storage are two main features to optimize in the design of routing protocols for wireless sensor networks. We address in this paper a particular class of WSNs, in which some nodes can be id-addressed, like in sensor-based guidance systems. We propose a routing protocol based on a two-level, zone-based architecture. DV is applied in both intra-zone and inter-zone routing, based on the hop metric. No particular instrumentation of the network is required: sensors are location unaware, energy unaware, and no variation in their transmission power is assumed. We show performances of the proposed algorithm evaluating the overhead and the behavior of the algorithm when increasing the network size. Simulations using the characteristics of MICA2 sensors allowed us to estimate the energy consumption -almost 4*10 -4 % of the total battery capacity, and the memory capacity needed for the routing tables -almost 13% of the total memory. Another interesting result that we infer from the memory metric is the lower bound for the number of zones in the construction of the network architecture. Future work will analyze the feasibility of this algorithm in the context of an emulator or of a real sensor network platform experimentation.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Average number of sent packets per node during Routing Tables Construction for 500-node deployment

Fig. 3 .

 3 Fig. 3. Battery consumption based on the characteristics of MICA2 sensors

10 + 12

 12 * N nZ ≤ M EM RAM * M AX M EM P RCT G This gives a lower bound for the number of zones, nZ ≥ 12 * N M EM RAM * M AX M EM P RCT G -10 We neglected in our estimation the memory capacity for the border nodes. Meanwhile, memory constraints for border nodes can be included by varying judiciously the MAX_MEM_PRCTG constant.

  TABLE: complete the table or UPDATE TABLE: update the table), the zone table (InterZoneRT) and the final destination (finalDestId).

TABLE I THEORETICAL

 I MEMORY SIZE (IN BYTES) FOR ROUTING DATA STRUCTURES

http://www.j-sim.org/


Violeta Felea

Management of Real Time Constraints in Sensor Networks

Keywords: wireless sensor networks, real time issues, deadline, prioritized queues, real time routing, scheduler

The scope of this paper is to compare existing techniques proposed to manage real time (RT) communications in wireless sensor networks. MAC, network or cross layer solutions are presented and discussed in order to draw up a global view of system designs. Identifying drawbacks of existing works allows drawing future directions concerning QoS guaranteeing data delivery under time constraints.

I. RT CHALLENGE

Quality of service (QoS) in terms of real time aspects needs to be proposed in sensor networks especially when they concern solutions addressing critical applications. Most domain applications such as battlefield surveillance, disaster and emergency detection, deal with real time constraints. Compared with solutions in traditional distributed systems, approaches for sensor networks need to be adapted because of the following reasons. First, guaranteeing that communications meet real time constraints should not compromise other critical system properties, especially energy efficiency, robustness and scalability. Second, limited resources, computational or characterizing network (as bandwidth) impose supplementary restrictions.

We intend to compare techniques proposed by some representative existing work in regard to three classes of solutions. The first concerns approaches exclusively involved at MAC layer. The second addresses routing at network layer. The third tackles scheduling approaches either at MAC layer or at network layer or both, known as cross-layer techniques. Our comparative analysis completes the state of art proposed in [START_REF] Li | Realtime QoS support in wireless sensor networks: a survey[END_REF], is more precise than the survey on QoS in [START_REF] Stankovic | Real-time communication and coordination in embedded sensor networks[END_REF] and extends the real time addressed only in routing made in [START_REF] Zhan | A Survey on Real-time Routing Protocols for Wireless Sensor Networks[END_REF]. Moreover, specific comparison points help identifying rapidly differences between approaches. These are: the type of real time solution (soft or hard), scalability, the type of the MAC layer (involving the portability issue), the routing mechanism, the node addressing and the scheduling policy (prioritization, delaying). We have chosen two projects for each class of solutions: SWAN [START_REF] Ahn | SWAN: Service Differentiation in Stateless Wireless Ad Hoc Networks[END_REF] and I-EDF [START_REF] Caccamo | An Implicit Prioritized Access Protocol for Wireless Sensor Networks[END_REF] for MAC layer, RAP [START_REF] Lu | RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks[END_REF] and JiTS [START_REF] Liu | JiTS : Just-in-Time Scheduling for Real-Time Sensor Data Dissemination[END_REF] for scheduling and SPEED [START_REF] He | SPEED: A Stateless Protocol for Real-Time Communication In Sensor Networks[END_REF] and RPAR [START_REF] Chipara | Real-Time poweraware routing in sensor networks[END_REF] for routing.

We briefly present in the next section each of the mentioned approaches, section 3 gives comparative analysis for each class, while interesting directions to be exploited are discussed in section 4. The last section concludes this analysis.

II. RT APPROACHES

A. MAC layer I-EDF [START_REF] Caccamo | An Implicit Prioritized Access Protocol for Wireless Sensor Networks[END_REF] is a MAC protocol based on priorities, addressing hard real time traffic. It is built upon three principles: cellular network structure, a modified MAC layer and a deterministic EDF1 -type scheduling. The network organization in cells is meant to avoid contentions. Nodes are synchronized over a same frame and organize themselves into adjacent cells. Inter-cell communication is based on a synchronized TDMA scheme, while inner-cell communication is supported by earliest deadline first (EDF) schedule.

SWAN [START_REF] Ahn | SWAN: Service Differentiation in Stateless Wireless Ad Hoc Networks[END_REF] addresses soft real time communication through service differentiation, at MAC level. SWAN model is stateless (no flow state is conserved in nodes). Meanwhile, the flow state is maintained in the flow itself, in the packet header. SWAN deals with two types of flow: real time and best-effort. The real time constraints are preserved using three principles: control the real time session admission rate, delay the best effort flow and notify when congestion appears. Mainly, SWAN restricts best effort traffic in order to yield the necessary bandwidth required to support real time traffic.

The summary of these MAC approaches in regard to the management of the real time issue, the intervention layer, the routing protocol, its advantages and drawbacks, as well as the simulation tool is given in table I [START_REF] He | SPEED: A Stateless Protocol for Real-Time Communication In Sensor Networks[END_REF] is a stateless protocol which supports a soft real time communication service with a desired delivery speed across the network. The next hop is determined on the basis of the packet speed and its geographical position according to destination. The selection of the next hop is done in four stages. First, SNGF, a GF 2 -like routing protocol, considers, out of the neighbor nodes those which bring closer the packet to destination. Second, packet speed is computed for each of the previously retained nodes. The speed is calculated by dividing the advance in distance from the next hop by the estimated delay to forward a packet to the next hop. Third, among all candidate nodes, those which assure a speed to the packet greater than a referential speed are considered for the last step, which chooses the node having the greatest speed. If there is no neighbor which can support the desired speed, packets are dropped.

RPAR [START_REF] Chipara | Real-Time poweraware routing in sensor networks[END_REF] tackles energy issue in the routing policy to support real time communication. RPAR assumes that a soft deadline is assigned to each packet by the application, specifying the desired bound on the end-to-end delay of the packet. The main idea is to combine optimization of both energy consumption and message latency: tight deadline packets are routed with lower latency by increasing transmission power; conversely, lax deadline packets may be routed with lower transmission power allowing energy saving. This approach is similar to SPEED in assigning speed to packets depending on the packet's deadline. Moreover, RPAR integrates energy metric in the choice of the neighbor which meets speed requirement. Unlike SPEED, RPAR protocol operates at an adaptive transmission power level.

The summary of these routing approaches is given in table II

C. Scheduling

RAP [START_REF] Lu | RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks[END_REF] is built up of a key-component, the packet scheduler, which assigns priority to packets depending on its requested velocity. Assuming that each sensor knows its location, velocity can be computed locally on the basis of Euclidean distance to destination and the packet's deadline. 2

Geographic Forwarding

A packet with higher requested velocity is assigned a higher priority (each priority corresponding to a range of requested velocities). Priority queues are implemented at network level. RAP maintains multiple FIFO queues each corresponding to a fixed priority level. The packet having received a specific priority (based on its requested velocity) is inserted to the FIFO queue corresponding to its priority. The MAC layer is prioritized in order to include packet priorities.

JiTS [START_REF] Liu | JiTS : Just-in-Time Scheduling for Real-Time Sensor Data Dissemination[END_REF] uses a different technique based on packet delay, according to the distribution of the slack-time (which is the available time before the deadline expires) across hops. Assuming that every packet knows its deadline, a delay per packet is computed as the difference between its deadline and the time globally spent in packet transmission, averaged by the number of hops. Time spent in end-to-end communication is estimated using the time spent in the MAC queue and the real transmission time on the medium. The delay time is used to order the waiting queue. It may be either static, fixed at the initiator of the packet, or dynamic, recomputed upon arrival at each intermediate node. The dynamic approach adjusts the requested velocity of the packet depending on its actual progress. Delaying packets assures that periods of congestion are avoided (by anticipating deadlines), while respecting real time policy.

The summary of these scheduling approaches is given in table III

IV. RT MANAGEMENT SYNTHESIS

We identify major design information which needs to be exploited in order to offer real time guarantees. As far as the mechanisms involved, new directions are still open. Obviously, evaluation metrics are commonly accepted and listed here.

A. Exploited information

Real time communications can be taken into account in wireless sensor networks, based on different information, which we group in four categories:

• time,

• distance and localization, • contextual, • derived. Time is essential and is available under different forms: the deadline imposed by the application, the time estimation of a hop communication (either between direct neighbors or for end-to-end paths). The former should be correlated with the network size (in order to be able to respect deadlines) while the latter is generally estimated by time measure of one hop communication or in a predictive manner.

Distance and localization information is mainly computed based on the number of hops or on Euclidean distance (using GPS technology or estimation of signal power). These mainly provide geographic knowledge about the next hop in regard to destination.

Contextual information concerns the environment (medium, density, etc.) or nodes. We identify, for example, the packet size, the available bandwidth, the node energy and also the presence of congestions, or contentions.

Derived information is extracted from previous information and from application parameters. For example, velocity in SPEED or RAP depends on time and Euclidean distance, or the slack-time in JiTS depends on deadline.

B. Issues and challenges in RT approaches

Definitely, real time frameworks designed to assure that packets transmitted in wireless sensor networks meet deadlines are oriented towards soft solutions. Hard real time systems, assuming that the arrival of a message after its deadline is system failure, are difficult to achieve. Logically, only MAC layer modifications are suitable for hard real time service, but diminish scalability and portability.

Two main issues concern real time communication in sensor networks: information recovery for real time management and data delivery.

Information is generally local, which allows high network scalability. Delivery deadlines are time constraints; therefore, estimation of communication time between neighbor sensor pairs is required in order to choose the best route meeting the deadline.

Data delivery should address three decisions: on the next hop, on the next packet to deliver and on the transmission moment. The choice of the next hop is generally based on geographic information. Node position awareness is definitely good as it presents low management cost, because computation is immediate, based on neighbor positions and neighbor knowledge alone is required. Thus, this technique does not suffer from route discovery delay and selected paths to destination are generally the shortest. Meanwhile, geographic forwarding is known to behave poorly to voids. It fails to discover routes in this context, and in the worst-case scenario, it may even fail to find paths, even if paths exist. Large voids make out of the Euclidean metric a poor approximation of the path length.

The choice of the packet to deliver is generally based on either the delay technique or on prioritization technique. Even though conceptually, two different techniques are identified, the former is implemented using priorities. The default queuing algorithm (FIFO) is insufficient, as it does not make decisions based on priorities. A FIFO queue would cause packet drops, even for high priority packets, due to capacity limitation. Instead, Priority Queuing (PQ) is suggested as one of the applicable sensor approach to meet the desired QoS for real time traffic.

The choice of the transmission moment depends completely on the MAC layer.

Two main challenges concern packet delivery in sensor networks with respect to real time communications: what about delivery in GPS-free systems, and integrating priority queues while still assuring portability? Node geo-localization may be an expensive mechanism for largely-deployed networks. The GPS technique may be partially used (beacon nodes alone are location-aware), but these strategies may generate important computation overhead and error. Moreover, GPS solutions are not available in any type of environment. Priority queues are generally implemented in the MAC layer. MAC level solutions require re-engineering of the sensor radio hardware and firmware, making deployment difficult and potentially causing interoperability problems with hardware supporting different MAC protocols.

Existing works partially integrate the following factors which may influence real time communications:

• energy -the most critical resource in sensor networks.

Limited by the capacity of batteries, sensor lifetime need to be maximized, not only to assure environment sensing, but also to assure packet relay in multi-hop sensor networks. Decreased network connectivity may result in disconnected sub-networks or in void appearance. Evenly distributed energy drain may help to increase the network lifetime. The challenge here concerns methods allowing energy estimation. Network energy efficiency while assuring real time communications is a trade-off. We identify two main directions in integrating energy in these constraint and QoS systems: energy-awareness and energy-exploitation. Only the first is currently proposed in existing works, either adapting transmission power, or scheduling judiciously activity and sleep periods for sensors [START_REF] Toscano | An Energy-Efficient Real-Time Communication Framework for Wireless Sensor Networks[END_REF]. Energy-awareness makes systems aware of the energy challenge, trying to minimize energy consumption. Energy-exploitation solutions would directly use energy information as decisional joint criteria. • bandwidth -a constraint which directly affects packet deadlines. Under load exceeding the available network bandwidth, the network should respond by either discarding packets or by queuing them, avoiding congestion periods, while still assuring at least partial packet delivery in the requested deadlines. This design consideration may be interesting especially in heterogeneous contexts. • power control -small-range multi-hop communication is proved less costly in energy than long-range ones.

Meanwhile, the increased number of hops expands the packet delay by accumulation. A trade-off is expected in order to assure minimum delay due to multi-hop transmissions and minimum energy consumption due to modified transmission power. This parameter is thus closely related to the energy parameter. Transmission power control is rarely exploited because it requires specific hardware which is not so widely integrated in sensor design.

C. Evaluation metrics

In supporting real time QoS for wireless sensor communications, several performance metrics are particularly interested to be evaluated:

• end-to-end delay is defined by the time taken for a packet to be transmitted across the network from source to destination,

• deadline miss ratio is defined by the number of packets which miss their deadlines over the number of initiated packets, • packet drop ratio is defined by the number of packets which have been dropped before reaching destination over the number of initiated packets, • delivery ratio is defined by the number of packets delivered over the number of initiated packets.

V. CONCLUDING REMARKS

One parameter of quality of service is providing time guarantees for data delivery. This is particularly the case for wireless sensor networks when used in surveillance, detection of particular environmental events, generally critical. The inherent trade-off between guaranteeing that communications meet real time constraints and assuring energy-efficiency, robustness and scalability is characteristic for these networks. In this article, we identify the major tendencies of real time management in wireless sensor networks and propose future directions in this context.

  .

		I-EDF	SWAN
	RT management	avoid contentions	delay the best-effort traffic
		and collisions	to accelerate the real time
	layer	MAC	MAC
	routing protocol	multi-hop	AODV or IP
	advantages	hard real time,	stateless,
		no packet loss	delaying approach
	drawbacks	modified MAC layer and	modified MAC layer
		specific network structure
	simulator	NS-2	NS-2

  .

		SPEED	RPAR
	RT management	keep desired speed	keep dynamic speed
		between nodes	based on adaptive
			transmission power
	layer	network	network
	routing protocol	DV based	GF
	advantages	stateless,	power-aware,
		void avoidance,	void avoidance,
		best effort MAC-layer no periodic exchange
	drawbacks	greedy algorithm+	not adapted to
		GF	congestion
	simulator	GloMoSim	Prowler
		TABLE II
		SUMMARY RT ROUTING APPROACHES

  .

		RAP	JiTS
	RT management	assign priorities to packets	packet delaying,
		depending on their deadline	slack-time assigned
		and distance to destination	depending on deadline
	layer	MAC and network	MAC
	routing protocol	GF + greedy	LS
	advantages	"local emergency"	avoids latency due to
		of packets	contention and congestion
	drawbacks	localization-aware and	small precision in the
		modified MAC	hop time estimation
	simulator	GloMoSim	NS-2

TABLE III SUMMARY

 III OF RT SCHEDULING APPROACHESIII. COMPARATIVE ANALYSISWe make a synthesis of different real time approaches, regarding its class (hard or soft), its scalability, the type of MAC layer involved, the type of node addressing (IPbased, ID-based, query-based, geographic, attribute-based), the type of the routing protocol, the scheduling policy (priority on packets). MAC approaches are summarized in table IV, scheduling approaches in table V and routing approaches in table VI.Experimental results for these approaches are difficult to compare for two main reasons. First, implementation tools are not homogeneous (GloMoSim, Prowler, NS-2). Second, and most important, parameters of the simulation environment (radio transmission range, network bandwidth, data packet size, packet deadline, packet rate) are rarely similar.

		SWAN	I-EDF
	RT	soft	hard
	scalability	good	moderate
	MAC layer	DCF	EDF + frame sharing
	node addressing	IP	ID
	routing	AODV/IP	multi-hop
	scheduling policy	-	EDF/no contention

TABLE V COMPARATIVE

 V ANALYSIS FOR RT SCHEDULING APPROACHES

Earliest Deadline First


Claude Boulay
email: claude.boulay@lcpc.fr

Jean Luc

Jean-Michel Michel Torrenti

Jean-Luc André

may come     Draft operating protocol to determine the level of heat released during cement hydration on a concrete specimen placed in quasi adiabatic calorimeter designed for concretes (QAB) : Note technique

Testing principle ■

The QAB calorimeter method consists of placing a specimen Ø 16 cm × 32 cm high at the center of a heat-insulated box in order to determine the quantity of heat released. At any given time, the heat released by hydration equals the cumulative heat input into both the calorimeter and specimen plus the heat that has dissipated to the outside since the initial time.

references Semi-adiabatic calorimeters are calibrated according to a protocol established within the network of LCPC Laboratories, indicated by the code Pro Q-E (Langavant-type calorimeters, verification). This protocol makes reference to the following documents:

InstrumentAtIon AnD DevIce chArActerIstIcs

Calorimeter ■

The calorimeter is composed of a PVC box, insulated using polyurethane foam. The specimen housing, located at the center of the box, has been designed for placement inside an intermediate steel cylinder. An insulated cover provides access for the cable connecting the temperature probe.

The calorimeter diagram is included in Appendix 1. To ensure adequate test reproducibility, the thicknesses of polyurethane foam insulating layers need to be compliant to the greatest extent possible. Rubber type joints provide the necessary seal. The calorimeter bases are essential for ventilation beneath the device, so that the temperature of the sidewall is determined by the ambient air. Without this precaution, the heat loss coefficient derived through calibration might have to be modified. It has even been recommended to install the calorimeter on a table at a height that allows for stirring on the under-surface, yet low enough to easily place the specimen inside the device (i.e. a height of 40 ± 5 cm).

To conduct a test, two calorimeters are introduced; a control specimen is positioned inside a QAB calorimeter identical to the calorimeter holding the fresh concrete specimen. The control specimen's internal temperature is close to ambient temperature and evolves with fluctuations in ambient temperature according to a time constant that is nearly the same as for the fresh concrete specimen.

Temperature measurement instrumentation ■

The temperature probes required to conduct measurements contain platinum resistors (100 Ω at 0°C), which are to be calibrated according to standard procedure. The probes are connected to their conditioning box, which in turn is connected to a data storage system.

The power dissipated by the Joule effect of this platinum probe must not exceed 1 mW (with a maximum current of 1.5 mA). It is recommended that this current only be applied when conducting measurements.

The maximum cross-section of wires running through the cover must remain less than 0.2 mm 2 per conductor for the thermocouples and 0.25 mm 2 per conductor for the platinum probes.

The expanded uncertainty (expansion factor k = 2), which is applicable to the entire temperature measurement chain, must be less than 0.2°C.

The temperature probes are calibrated according to the procedure specified in the "References" section.

experImentAl cAmpAIGn

In the vicinity of the calorimeters, air temperature must be within ±1°C of the initial test temperature. The ambient air temperature probe is used to control test room settings; this probe needs to be positioned at the center of an aluminum block that serves to limit rapid temperature fluctuations caused by air (sized as Ø 20 mm × 80 mm high). An active calorimeter receives the specimen to be tested while an identical one is used as the control device. A chemically-inert concrete specimen (aged for more than 3 months) is placed inside the control device.

Test requirements include:

2 QAB calorimeters featuring identical heat loss coefficients and heat capacities to within 5%; a compensation specimen fabricated with a concrete similar to the tested design (i.e. equivalent specific heat capacity values), yet remaining in its cardboard mold; three calibrated temperature probes; -a data acquisition system; a cardboard mold along with its plastic cover. -Prior to conducting the test, room temperature is set at the initial test temperature value. The calorimeters are laid out in the test room, then opened so as to remove the control specimen from its calorimeter. This configuration allows placing the calorimeters and control specimen at the proper test temperature.

Note: The user may elect to collect multiple control specimens (between 2 and 5), under the premise of being able to respond to all types of situations. As an example, a concrete mix design that contains limestone aggregates and a large quantity of water offers a higher specific heat capacity than that of a design containing siliceous aggregates with little water. This aspect of the test protocol is left up to the operator to implement.

The temperature probes are routed through the cover cable gland of both calorimeters and then through the steel mold cover opening. The plastic cover of the control specimen is perforated exactly at the center with an 8-mm diameter. A copper tube (170-mm length, 10-mm outside diameter, 1-mm thickness), fitted with a flared nipple (or a plastic, quick-clamping collar) at one end and hermetically closed at the other end, is run through the mold cover opening (it is assumed that the same conditions have been adopted for the control specimen).

The mold is then weighed, to within the nearest gram, along with its cover and the copper tube.

The concrete is mixed. The date and time of adding water are carefully recorded. At the mixer output, the concrete temperature (T concrete (0)) is measured and recorded as well. The concrete is placed into the mold using state-of-the-art practices. Once the mold is nearly filled, the copper tube is penetrated, under the effect of vibration (either internal or external to the mold), until the cover is readjusted onto the top of the specimen. This assembly is then weighed to know the actual concrete mass (m b ) being set. An adhesive tape is wrapped around the cover in order to minimize evaporation and condensation inside the calorimeter. Next, the specimen is transported into the test room.

The calorimeters are placed on the low table, as outlined in the section "Calorimeter". Specimens are inserted inside the calorimeter steel shell and covered by the steel cap. The temperature probes are positioned inside the copper tubes. Oil is poured into these tubes up to the rim. Modeling putty is applied around the steel cap opening. The calorimeter covers are installed and attached; additional putty is applied where the cable gland gets inserted. The data acquisition system is activated. The measurement frequency interval is set at 10 to 15 min (for between 96 and 144 measurements/day). A display of temperature deviation between the test specimen and control specimen is desirable. The data recorded are: date and time of reading (format: dd/mm/yy hh:mm:ss), temperatures T concrete , T control and T amb .

Both the hydration kinetics and duration of complete concrete hydration are highly variable. No rule has ever been adopted to dictate when these measurement recordings are to be terminated. At a certain point in time, depending on temperature measurement performance, heat release can no longer be detected; in this case, it is assumed that the bulk of the hydration reaction has already occurred. As of this point, continuing to record temperatures is of no further utility. If the test is stopped before reaching this point, then the test result consists of the value of heat released at this stop time. Choosing when to halt measurements is thus incumbent upon the operator. The indication of temperature deviation derived from the data acquisition chain offers a useful control value for this particular purpose. It is possible, for example, to determine the stop point once this deviation value declines to less than twice the expanded uncertainty of the temperature measurement.

DetermInAtIon of heAt of hyDrAtIon

Spreadsheet-based calculation of heat released ■

In the QAB calorimeter, a proportion of the heat released by cement hydration increases specimen temperature, while another proportion increases the calorimeter temperature and the final proportion is discharged to the outside. Such a breakdown can be expressed as follows:

(1)

The protocol for using this expression in a spreadsheet application on a set of discrete data is described below.

Initially a row, containing the date and time of casting, mixer output temperature T concrete (0), control specimen temperature (set equal to the first temperature measured on this specimen) and a similarly-treated ambient temperature value, is added on top of the actual measurement recordings.

A column entitled "concrete age" or time t is added as well. This time parameter is calculated in hours, as the date and time of each measurement minus the date and time recorded when casting first took place.

C tot is the sum of heat capacities of both the concrete (C concrete ) and calorimeter (C cal ). The calorimeter heat capacity is given in the calibration report; its order of magnitude for the dimensions shown in the figure in Appendix 1 equals 3,400 J/°C. The heat capacity of concrete is calculated according to the following expression:

C concrete = µ s (m c + m s + m g ) + µ l m e (3)
• µ s is the specific heat capacity for solids in the mix (cement, sand and aggregates) and equals approx. 800 J °C-1 kg -1 .

• µ l = is the average specific heat capacity 1 for the mixing water and equals 3,800 J °C-1 kg -1 .

• m c , m s , m g , and m e are respectively the masses of cement, sand, aggregates and water of the specimen itself.

These masses are obtained from both the concrete composition and specimen mass. With this setup, the first step consists of determining the mass proportion of each concrete component (component mass per m 3 of concrete divided by the total mass of all components per m 3 of concrete). To ascertain the masses of all specimen components, the next step simply requires multiplying the mass proportions of each component by the specimen mass.

The last term of Equation ( 1) is calculated from the initial time 0, i.e. when it is considered that zero heat has been released (heating due to anhydrate dissolution is neglected during the first few instants). For each subsequent step of rank k, the basic energy loss is calculated as follows:

, which is then added to the value obtained for rank k -1.

The q(t) curve then yields the expected result.

Other information can also be drawn from this curve (see [2, 3, 4]).

uncertAIntIes In the heAt releAse DetermInAtIon

The parameters exerting significant influence on this result are the specimen and control temperature measurements, as well as the heat loss coefficient of the calorimeters.

Briffaut M 3

. , BenBoudJeMa f., torrenti J. M., nahas g., BLPC n° 278, 2010, pp. 5-18. Martin r. p. 4

, toutLeMonde f., « Mise au point d'une cure thermique représentative de l'échauffement d'une pièce massive de béton », BLPC n° 278, 2010, pp. 49-63.

  Pro Q-S1 (Metrological function) -Pro Q-S2 (Langavant calorimeters, means of verification) -Standard NF EN 196-9 (Cement test methods -Part 9: Heat of hydration -semi-adiabatic method) MET E2-1-4-3 (Metrology of digital thermometers: Calibration). -QAB calorimeters are calibrated according to a procedure analogous to that of the Langavant calorimeters [1].

  denote the following: C concrete [in J/°C]: heat capacity of the concrete alone (product of mass times specific heat capacity), C cal [J/°C]: heat capacity of the calorimeter, obtained from the calibration step, C tot [J/°C]: total heat capacity = C concrete + C cal t -[hours]: time elapsed during the QAB test, T concrete [°C]: temperature of the fresh concrete specimen, T ext [°C]: exterior temperature, T control [°C]: temperature of the hardened concrete control specimen, q(t) -[J]: heat release at time t θ -(t) [°C or K] = T concrete -T control : deviation in temperature between the fresh concrete specimen and the control specimen at time t α[J/h/°C]: heat conduction coefficient, which can be expressed as a function of θ (α = -a + b θ), where coefficients a and b are obtained by calibration, m b : mass of concrete poured into the cardboard mold.

  With a set of elementary uncertainties u xi , such as those listed in the table below, the standard uncertainty on heat release equals approx. 5% of the release through four weeks of testing [2].BLPC n° 278, 2010, pp. 43-45.

		Parameter	Nominal value	u xi	Units
		Mass of the empty mold	0.346	0.0002	kg
		Mass of the filled mold	15.262	0.005	kg
		Cement mass	2.171	0.005	kg
		Water mass	1.176	0.0002	kg
		Sand mass	4.722	0.005	kg
		Gravel mass	6.846	0.005	kg
		C cal	3266	32.66	J/°C
		α	369.8	3.2	J/°C/h
		Solid specific heat capacity	750	7.5	J/°C/kg
		Liquid specific heat capacity	3760	37.6	J/°C/kg
		T concrete	T concrete (t)	0.1	°C
		T control (t)	T control (t)	0.1	°C
	references
	1	andré J.L. , saintiLan r., « Étalonnage des
		caissons QAB », BouLay C.

2 , torrenti J.M., andré J.L., saintiLan r., « Calorimétrie quasi adiabatique pour bétons : facteurs d'influence », BLPC n° 278, 2010, pp. 18-36.

The specific heat capacity of water equals 4,180 J °C-1 kg-1, yet this value differs for water combined with hydrates.

AppenDIx 1:

Scaled diagram of a QAB calorimeter PT100


Calibration of a QAB calorimeter

TesT principle

Executing the quasi-adiabatic test on concrete (called the QAB test) requires accurate knowledge of both the total heat loss coefficient α and heat capacity µ of the calorimeters introduced, with these two parameters capable of being determined by calibration. This method, adapted from Standard NF EN 196-9 (entitled "Cement test methods -Part 9: Hydration heat -The semi-adiabatic method"), consists of replacing the test sample inside the calorimeter to be calibrated with a same-sized calibration cylinder equipped with an electrical resistance. By applying a known potential difference to this resistance so as to trigger a temperature rise, the amount of applied electrical energy corresponds to both the temperature rise taking place in the calorimeter and the heat dissipated towards the atmosphere. Heat loss can then be calculated based on the electrical energy required to maintain constant temperature.

As a next step, the heat capacity is determined by measuring the rate at which temperature is decreasing inside the calorimeter once the electrical source has been disconnected.

InstrumentAtIon

In order to apply this method, the following instruments are required: a digital multimeter, used to measure both the calibration cylinder resistance and test voltages; a temperature measurement chain composed of Pt100 4-wire probes; a stabilized power source; a calibration cylinder (see -Fig. 2b), containing an aluminum specimen with an identical shape to the test specimen (i.e. Ø 16, H = 32 cm) and a heating resistance of approx. 2,500 ohms with a very low coefficient of electrical resistivity (a constantan or manganin wire). The resistance connection wires are designed with a small cross-section (max. 0.05 mm 2 ) to avoid heat leaks; a calorimeter used as a control device, equipped with an inert cylinder of an identical design to the calibration cylinder, yet without a heating resistance; a climate-controlled room set at a temperature of between 19 and 21°C and programmed to remain stable to within ± 0.5°C. Jean-Luc ANDRE jean-luc.andre@developpementdurable.gouv.fr operAtIng proCedure

Preliminary conditions ■

The calorimeter to be calibrated is placed immediately adjacent to the control calorimeter in the climate-controlled room (Fig. 1). Both devices are positioned on a table to withstand the influence of heat exchange conditions at floor level, which is where conditions can differ considerably from one testing room to another.

A calibration cylinder, connected to the power supply, is placed inside the calorimeter to be verified, while an inert cylinder is placed in the control calorimeter. A platinum resistance probe is also positioned at the center of each cylinder in the test set-up.

The opening on the calorimeter cap (compression gland) (see Figs. 1 and2a), which serves to route the connection wires of both the calibration cylinder and the resistance probe, is hermetically sealed so as to prevent convection currents between the calorimeter interior and the ambient environment.

  Figure 1View of the two calorimeters

Determination of the total heat loss coefficient α ■

The application of a voltage at the calibration cylinder resistance terminals causes this cylinder to warm by means of the Joule effect.

The total heat loss coefficient α of the calorimeter is obtained by measurement, during an established steady-state mode, using: the amount of heat released by the Joule effect inside the calibration cylinder warming of this cylinder in comparison with the temperature of the inert cylinder placed in the control calorimeter.

Once the steady state has been reached (the time required to achieve thermal equilibrium is on the order of 14 days), the heat generated is completely dissipated towards the external environment.

The loss equation can therefore be written as follows:

or alternatively: = 3600 The constants a and b are calculated using the least squares method, with the linear correlation coefficient needing to exceed 0.97. Figure 3 shows an example of this result.

Figure 3 Sample calibration line

Determination of heat capacity µ ■

The calorimeter heat capacity µ is measured by means of the spontaneous cooling method when the expression of total heat loss coefficient α is known.

Once the heat capacity C T of the calorimeter containing a cylinder with known heat capacity C E (i.e. a calibration cylinder) has been determined, it then becomes possible to deduce the heat capacity of the empty calorimeter µ.

During cooling (after shutting off the power supply), the heat exchange equation can be written as follows:

-C T dθ = α θ • dt = (a + bθ) θ • dt which leads to: with: t (in hours) = cooling time from the initial instant (time 0) θ 0 , α 0 = temperature rise and total heat loss coefficient at time 0 θ t , α t = temperature rise and total heat loss coefficient at time t Four measurements of θ t are recorded for cooling periods of 24, 26, 28 and 30 hours.


Mechanical effect of adsorption Carbon sequestration and swelling of coal

Laurent BROCHARD Université Paris-Est. Laboratoire Navier (UMR CNRS 8205). Ecole des Ponts ParisTech

CONTEXT -CARBON SEQUESTRATION AND SWELLING OF COAL

In most scenarios for stabilization of atmospheric greenhouse gas concentrations [...] CCS contributes 15 -55% to the cumulative mitigation effort worldwide

Conventional poromechanics fails to explain the swelling

Helmholtz free energy of the solid matrix of a saturated isotropic porous medium:

f solid = 1 2 K + b 2 N 2 -bN ϕ + N 2 ϕ 2 + i,j∈{1,2,3} G 2 e 2 ij
where: is the volumetric strain, ϕ the change of porosity, G the shear modulus, and N the Biot modulus.

e ij the deviatoric strains, K the bulk modulus, b the Biot coefficient,

The volumetric stress is obtained with the state equation:

σ = ∂f solid ∂ ϕ,e ij = K -bP (Coussy (2010))
Unjacketed experiment:

P CO 2 small P CO 2 high
Conventional poromechanics predicts a shrinkage, and the same whatever the gas!

σ = -P ⇒ = - 1 -b K P < 0
Objective: Understand the physics of swelling and predict the permeability loss

CONVENTIONAL POROMECHANICS EXTENDED TO SURFACE EFFECTS

Bulk density

Γ = N s /A Density Solid
Contrary to CO 2 and CH 4 , helium behaves as predicted by poromechanics. The difference stems from the adsorption (low for Helium, high for CO 2 and CH 4 ) which may have an impact on the mechanics of a solid, since it modifies the fluid-solid interface energy γ FS (Gibbs adsorption equation, at fixed temperature and interface area): dγ FS = -Γdµ. The mechanical impact of adsorption can be sketched in the case of a thin plate: Helmholtz free energy of the (solid matrix + interface):

f solid = 1 2 K + b 2 N 2 -bN ϕ + N 2 ϕ 2 + i,j∈{1,2,3} G 2 e 2 ij +γs
s the specific surface., and γ FS the fluid-solid interface energy.

Å -1 CO 2 CH 4
CO 2 and CH 4 density profiles on the surface of coal matrix were obtained by molecular simulation.The swelling predicted by the model (Equation 1) is compared to the experimental swelling.

POROMECHANICS OF NANOPORE ADSORPTION

Explanation: Adsorption occurs also in the nanopores of the solid matrix.

Guideline for the poromechanics of nanopore adsorption: Use known and measurable quantities only. Questionable quantities (porosity, specific surface) should not intervene.

Behavior law of a porous solid subjected to a fluid under any form (bulk, surface adsorption, nanopore adsorption...)

σ = df S d - ∂ ∂ µ -∞ N V 0 dµ µ ( 2
)
V 0 is the volume of the porous solid under unstressed conditions, N is the number of fluid molecules whatever their state (adsorbed in nanopores, on surfaces, bulk...). f S = F S /V 0 and F S is the free energy of the sole solid, that is when there is no fluid molecule in the pores. f S = 1 2 K 2 for an elastic solid.

Requires to know the adsorbed amount as a function of both µ and . 2 × 10 -11 1 × 10 -11 7.5 × 10 -12 5 × 10 -12 3 × 10 -12 2 × 10 -12 1 × 10 -12 7.5 × 10 -13 5 × 10 -13 2.5 × 10 -13 1 × 10 -13 1 × 10 -14 0 The excess stress directly computed (virial estimate) and the excess stress predicted by the proposed thermodynamics (Equation 2) are consistent.

Case of a 1D chain subjected to fluid adsorption

σ -σ (f = 0) = - ∂ ∂ µ -∞ N V 0 dµ µ -6 -5 -4 -3 -2 -1 0

(

  From: IPCC report on Carbon Capture and Sequestration (2005))

  A d s o r b e d la y e r

σ

  = K -bP+ σ s ∂s ∂ P where σ s = γ + s ∂γ ∂s is the interface stress. (Vandamme et al. (2010))Predicts a possible swelling, specific to each fluid:

Fugacity

  from Ottiger et al. (2008))

The model does not capture the experimental swelling

	Volumetric strain, %	0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2	Experimental swelling CH 4 Experimental swelling CO 2 Predicted swelling CH 4 Predicted swelling CO 2
		0
		0	1	2	3	4	5	6
				Bulk pressure, MPa

(Experimental data from Levine (1996))

January 27 th , 2011