We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, making link quality measurements with the previous relay at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed on the line with a sink at the start of the line. In this paper, we extend our earlier work on the problem (see [1]) to incorporate two new aspects: (i) inclusion of path outage in the deployment objective, and (ii) permitting the deployment agent to make measurements over several consecutive steps before selecting a placement location among them (which we call backtracking). We consider a light traffic regime, and formulate the problem as a Markov decision process. Placement algorithms are obtained for two cases: (i) the distance to the source is geometrically distributed with known mean, and (ii) the average cost per step case. We motivate the per-step cost function in terms of several known forwarding protocols for sleep-wake cycling wireless sensor networks. We obtain the structures of the optimal policies for the various formulations, and provide some sensitivity results about the policies and the optimal values. We then provide a numerical study of the algorithms, thus providing insights into the advantage of backtracking, and a comparison with simple heuristic placement policies.
In the framework of @cite_1 , the deployment agent can only move forward. In the present paper we introduce backtracking," which permits the deployment agent to compare the link qualities over several potential placement locations before deploying the relay at any one of them.
@cite_1
2952542244
60 GHz millimeter-wave networks have emerged as a potential candidate for designing the next generation of multi-gigabit WLANs. Since the 60 GHz links suffer from frequent outages due to blockages caused by human mobility, deploying 60 GHz WLANs that can provide robust coverage in presence of blockages is a challenging problem. In this paper, we study blockage-aware coverage and deployment of 60 GHz WLANs. We first show that the reflection profile of an indoor environment can be sensed using a few measurements. A novel coverage metric (angular spread coverage) which captures the number of available paths and their spatial diversity is proposed. Additionally, it is shown that using relays can extend the coverage of the AP at a lower cost and provide added spatial diversity in the available paths. We propose a heuristic algorithm that determines the AP and relay locations while maximizing the angular spread coverage metric for the clients. Our testbed-based evaluation shows that for five different rooms, our proposed deployment can guarantee an average connectivity of 91.7 , 83.9 , and 74.1 of client locations in the presence of 1, 3 and 5 concurrent human blockages respectively, substantially increasing the robustness of 60 GHz links against blockages. We consider two variants of the task of spreading a swarm of agents uniformly on a ring graph. Ant-like oblivious agents having limited capabilities are considered. The agents are assumed to have little memory, they all execute the same algorithm and no direct communication is allowed between them. Furthermore, the agents do not possess any global information. In particular, the size of the ring (n) and the number of agents in the swarm (k) are unknown to them. The agents are assumed to operate on an unweighted ring graph. Every agent can measure the distance to his two neighbors on the ring, up to a limited range of V edges. The first task considered, is dynamical (i.e. in motion) uniform deployment on the ring. We show that if either the ring is unoriented, or the visibility range is less than @?n [email protected]?, this is an impossible mission for the agents. Then, for an oriented ring and V>[email protected]?n [email protected]?, we propose an algorithm which achieves the deployment task in optimal time. The second task discussed, called quiescent spread, requires the agents to spread uniformly over the ring and stop moving. We prove that under our model, in which every agent can measure the distance only to his two neighbors, this task is impossible. Subsequently, we propose an algorithm which achieves quiescent but only almost uniform spread. The algorithms we present are scalable and robust. In case the environment (the size of the ring) or the number of agents changes during the run, the swarm adapts and re-deploys without requiring any outside interference. This paper presents a novel approach to placing access points (APs) in wireless local area networks (WLAN). Unlike the traditional methods focusing on coverage maximization, we investigate how to place APs from the perspective of an indoor location system. We present a framework for linking the placement of APs and the positioning performance. Our algorithm, namely maximizing SNR (signal-to-noise ratio), aims at choosing a proper set of APs' locations so that the signal is maximized and the noise is minimized simultaneously. The proposed algorithm regards the location discriminant information as the signal and the degree of unstable measurements as the noise. Such numerical values provide a good theoretical formulation to measure the influence of the AP deployment. In our location system, we utilize such SNR ratio to deploy APs so as to reduce the positioning errors. Our location system is developed in a real-world WLAN environment, where the realistic measurements were collected. The experimental results show that the positioning accuracy is improved based on our deployment approach in different scenarios. Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters cloud and deployed on edge devices. This model deployment process can be challenging as the deployment environment and requirements can be substantially different from those during model development. In this paper, we propose a new ML development and deployment approach that is specially designed and optimized for inference-only deployment on edge devices. We build a prototype and demonstrate that this approach can address all the deployment challenges and result in more efficient and high-quality solutions. Our work is motivated by impromptu (or ''as-you-go'') deployment of wireless relay nodes along a path, a need that arises in many situations. In this paper, the path is modeled as starting at the origin (where there is the data sink, e.g., the control center), and evolving randomly over a lattice in the positive quadrant. A person walks along the path deploying relay nodes as he goes. At each step, the path can, randomly, either continue in the same direction or take a turn, or come to an end, at which point a data source (e.g., a sensor) has to be placed, that will send packets to the data sink. A decision has to be made at each step whether or not to place a wireless relay node. Assuming that the packet generation rate by the source is very low, and simple link-by-link scheduling, we consider the problem of sequential relay placement so as to minimize the expectation of an end-to-end cost metric (a linear combination of the sum of convex hop costs and the number of relays placed). This impromptu relay placement problem is formulated as a total cost Markov decision process. First, we derive the optimal policy in terms of an optimal placement set and show that this set is characterized by a boundary (with respect to the position of the last placed relay) beyond which it is optimal to place the next relay. Next, based on a simpler one-step-look-ahead characterization of the optimal policy, we propose an algorithm which is proved to converge to the optimal placement set in a finite number of steps and which is faster than value iteration. We show by simulations that the distance threshold based heuristic, usually assumed in the literature, is close to the optimal, provided that the threshold distance is carefully chosen. The behaviours of autonomous agents may deviate from those deemed to be for the good of the societ al systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, where these norms specify the obliged, permitted and prohibited behaviours of agents. Regulation can effectively be achieved through use of enforcement mechanisms that result in a net loss of utility for an agent in cases where the agent's behaviour fails to comply with the norms. Recognition of compliance is thus crucial for achieving regulation. In this paper we propose a generic architecture for observation of agent behaviours, and recognition of these behaviours as constituting, or counting as, compliance or violation. The architecture deploys monitors that receive inputs from observers, and processes these inputs together with transition network representations of individual norms. In this way, monitors determine the fulfillment or violation status of norms. The paper also describes a proof of concept implementation and deployment of monitors in electronic contracting environments. We study the problem of optimal sequential ("as-you-go") deployment of wireless relay nodes as a person walks along a line of random length (with a known distribution). The objective is to create an impromptu multihop wireless network for connecting a packet source to be placed at the end of the line with a sink node located at the starting point, to operate in the light traffic regime. As the deployment person walks along the line from the sink towards the source, at every step, he measures the channel quality to one (or more) previously placed relays, and places the relay nodes based on these measurements, so as to minimize either the sum power or the maximum power from the source to the sink node in the resultant network, subject to a constraint on the expected number of relays placed. For each of these two objectives, two different relay selection strategies are considered: (i) each relay communicates with the sink via its immediate previous relay, (ii) the communication path can skip some of the deployed relays. With appropriate modeling assumptions, we formulate each of these problems as a Markov decision process (MDP). We provide the optimal policy structures for all these cases, and provide illustrations, via numerical results, for some typical parameters. Wireless sensor networks (WSNs) consist of a large number of small, battery-powered wireless sensor nodes and thereby constrained with energy resource. The network as a whole must minimize the energy usage in order to enable untethered and unattended operation for an extended period of time. One fundamental way of reducing such energy usage and enhancing lifetime is judicial placement of sensor nodes within the network area. The present work proposes a lifetime-enhancing node-deployment strategy. Primarily more nodes are deployed towards the sink exploiting the fact that more energy is drained out from the nodes close to the sink. Further, locations which are approximately equi-distant from the sink are assigned different classes of priorities according to their responsibility of forwarding data of nodes located in the neighbouring cells. The principle of this strategy is justified by Lemma and corresponding proof. Exhaustive simulation is conducted to observe the impact of node distribution on network lifetime. The result is compared with one of the existing deployment strategies, which shows our scheme outperforms the existing one in terms of network lifetime. Getting new security features and protocols to be widely adopted and deployed in the Internet has been a continuing challenge. There are several reasons for this, in particular economic reasons arising from the presence of network externalities. Indeed, like the Internet itself, the technologies to secure it exhibit network effects: their value to individual users changes as other users decide to adopt them or not. In particular, the benefits felt by early adopters of security solutions might fall significantly below the cost of adoption, making it difficult for those solutions to gain attraction and get deployed at a large scale. Our goal in this paper is to model and quantify the impact of such externalities on the adoptability and deployment of security features and protocols in the Internet. We study a network of interconnected agents, which are subject to epidemic risks such as those caused by propagating viruses and worms, and which can decide whether or not to invest some amount to deploy security solutions. Agents experience negative externalities from other agents, as the risks faced by an agent depend not only on the choices of that agent (whether or not to invest in self-protection), but also on those of the other agents. Expectations about choices made by other agents then influence investments in self-protection, resulting in a possibly suboptimal outcome overall. We present and solve an analytical model where the agents are connected according to a variety of network topologies. Borrowing ideas and techniques used in statistical physics, we derive analytic solutions for sparse random graphs, for which we obtain asymptotic results. We show that we can explicitly identify the impact of network externalities on the adoptability and deployment of security features. In other words, we identify both the economic and network properties that determine the adoption of security technologies. Therefore, we expect our results to provide useful guidance for the design of new economic mechanisms and for the development of network protocols likely to be deployed at a large scale. One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness. Relay-assisted FSO systems were proposed as a means for remedying the effects of the various atmospheric impairments on the quality of the FSO signal. Conventional relay-assisted FSO systems, however, are designed around two basic assumptions: relays are buffer-free, and relays are stationary. This article proposes to improve the performance of the existing relay-assisted FSO systems by relaxing both of these highly restrictive assumptions through the integration of UAVs as buffer-aided moving relays into the conventional relay-assisted FSO systems. Specifically, two possible simple integration scenarios are proposed and analyzed through simulation. The obtained simulation results demonstrate the great potential associated with the proposed highly promising, innovative, hybrid FSO architecture. Given that high performance gains are observed under small buffer sizes, it becomes conceivable to employ buffer-aided moving relaying UAVs to serve a variety of other purposes. This includes, for instance, having these UAVs oversee the operation of amateur drones for potential misbehavior or wrongdoing within the area of their deployment. We consider transmission of packets in two-hop wireless ad hoc networks in which relay nodes are deployed between the source-destination pairs. Based on results from extreme value theory and product tails, we derive throughput scaling laws when opportunistic relay selection is performed. Assuming partial channel state information at each transmitter (CSIT) and decode- and-forward, half-duplex relays, we investigate how the per-hop throughput depends on the channel gain asymptotic distribution and the relay deployment. In dense networks with lambda t nodes per m 2 and fixed relay distances, we provide specific scaling laws for Rayleigh, lognormal, and Weibull fading, showing that the throughput is upper bounded by thetas(radic(lambda t )). Interestingly, with variable relay distances and location-aware relay selection, we analytically show that regularly varying channel distributions result in enhanced multi-relay diversity gain, achieving linear throughput scaling thetas(radic(lambda t )). We study the size of memory of mobile agents that permits to solve deterministically the rendezvous problem, i.e., the task of meeting at some node, for two identical agents moving from node to node along the edges of an unknown anonymous connected graph. The rendezvous problem is unsolvable in the class of arbitrary connected graphs, as witnessed by the example of the cycle. Hence we restrict attention to rendezvous in trees, where rendezvous is feasible if and only if the initial positions of the agents are not symmetric. We prove that the minimum memory size guaranteeing rendezvous in all trees of size at most nis i¾?(logn) bits. The upper bound is provided by an algorithm for abstract state machines accomplishing rendezvous in all trees, and using O(logn) bits of memory in trees of size at most n. The lower bound is a consequence of the need to distinguish between up to ni¾? 1 links incident to a node. Thus, in the second part of the paper, we focus on the potential existence of pairs of finiteagents (i.e., finite automata) capable of accomplishing rendezvous in all bounded degreetrees. We show that, as opposed to what has been proved for the graph exploration problem, there are no finite agents capable of accomplishing rendezvous in all bounded degree trees. Universal mobile telecommunication system (UMTS) networks should be deployed according to cost-effective strategies that optimize a cost objective and satisfy target quality-of-service (QoS) requirements. In this paper, we propose novel algorithms for joint uplink downlink UMTS radio planning with the objective of minimizing total power consumption in the network. Specifically, we define two components of the radio planning problem: 1) continuous-based site placement and 2) integer-based site selection. In the site-placement problem, our goal is to find the optimal locations of UMTS base stations (BSs) in a certain geographic area with a given user distribution to minimize the total power expenditure such that a satisfactory level of downlink and uplink signal-to-interference ratio (SIR) is maintained with bounded outage constraints. We model the problem as a constrained optimization problem with SIR-based uplink and downlink power control scheme. An algorithm is proposed and implemented using pattern search techniques for derivative-free optimization with augmented Lagrange multiplier estimates to support general constraints. In the site-selection problem, we aim to select the minimum set of BSs from a fixed set of candidate sites that satisfies quality and outage constraints. We develop an efficient elimination algorithm by proposing a method for classifying BSs that are critical for network coverage and QoS. Finally, the problem is reformulated to take care of location constraints whereby the placement of BSs in a subset of the deployment area is not permitted due to, e.g., private property limitations or electromagnetic radiation constraints. Experimental results and optimal tradeoff curves are presented and analyzed for various scenarios. To achieve better performance, we adopt a Heterogeneous Sensor Network (HSN) model. In many applications, the locations of some sensor nodes are controllable. In this paper, first we propose a novel density-varying deployment scheme for high-end sensors (H-sensors) in an HSN. The scheme solves the bottleneck problem in typical many-to-one sensor networks. We then study the optimal placement of H-sensors whose locations are controllable. The goal is to use the minimum number of H-sensors for ensuring successful data delivery, coverage and connectivity in a network for a given lifetime. We present an effective H-sensor placement scheme that can simultaneously achieve coverage, connectivity and data relay requirements while uses a small number of H-sensors. Both theoretical proofs and simulation results demonstrate that the proposed H-sensor placement scheme achieves very good performance. Deploying wireless sensor networks to provide guaranteed barrier coverage is critical for many sensor networks applications such as intrusion detection and border surveillance. To reduce the number of sensors needed to provide guaranteed barrier coverage, we propose multi-round sensor deployment which splits sensor deployment into multiple rounds and can better deal with placement errors that often accompany sensor deployment. We conduct a comprehensive analytical study on multi-round sensor deployment and identify the tradeoff between the number of sensors deployed in each round of multi-round sensor deployment and the barrier coverage performance. Both numerical and simulation studies show that, by simply splitting sensor deployment into two rounds, guaranteed barrier coverage can be achieved with significantly less sensors comparing to single-round sensor deployment. Moreover, we propose two practical solutions for multi-round sensor deployment when the distribution of a sensor's residence point is not fully known. The effectiveness of the proposed multi-round sensor deployment strategies is demonstrated by numerical and simulation results. The calculation of two-hop connectivity be- tween two terminals for randomly deployed wireless networks requires the joint probability distribution of the distances between these terminals and the terminal that is acting as a relay. In general the distances are not independent since a common terminal is involved. The marginal distributions for link distances are known for various random deployment models. However, the joint distribution of two or more link distances is not known. In this paper, the derivation of the joint distribution is given in general form and in a new form suitable for computation for a network of terminals randomly deployed in a square area. We study the problem of gathering at the same location two mobile agents that are dispersed in an unknown and unlabeled environment. This problem called Rendezvous, is a fundamental task in distributed coordination among autonomous entities. Most previous studies on the subject model the environment as an undirected graph and the solution techniques rely heavily on the fact that an agent can backtrack on any edge it traverses. However, such an assumption may not hold for certain scenarios, for instance a road network containing one-way streets. Thus, we consider the case of strongly connected directed graphs and present the first deterministic solution for rendezvous of two anonymous (identical) agents moving in such a digraph. Our algorithm achieves rendezvous with detection for any solvable instance of the problem, without any prior knowledge about the digraph, not even its size. In this paper we study efficient rendezvous of two mobile agents moving asynchronously in the Euclidean 2d-space. Each agent has limited visibility, permitting it to see its neighborhood at unit range from its current location. Moreover, it is assumed that each agent knows its own initial position in the plane given by its coordinates. The agents, however, are not aware of each others position. The agents possess coherent compasses and the same unit of length, which permit them to consider their current positions within the same system of coordinates. The cost of the rendezvous algorithm is the sum of lengths of the trajectories of both agents. This cost is taken as the maximum over all possible asynchronous movements of the agents, controlled by the adversary. We propose an algorithm that allows the agents to meet in a local neighborhood of diameter O(d), where d is the original distance between the agents. This seems rather surprising since each agent is unaware of the possible location of the other agent. In fact, the cost of our algorithm is O(d2+e), for any constant e > 0. This is almost optimal, since a lower bound of Ω(d2) is straightforward. The only up to date paper [12] on asynchronous rendezvous of bounded-visibility agents in the plane provides the feasibility proof for rendezvous, proposing a solution exponential in the distance d and in the labels of the agents. In contrast, we show here that, when the identity of the agent is based solely on its original location, an almost optimal solution is possible. An integral component of our solution is the construction of a novel type of non-simple space-filling curves that preserve locality. An infinite curve of this type visits specific grid points in the plane and provides a route that can be adopted by the mobile agents in search for one another. This new concept may also appear counter-intuitive in view of the result from [22] stating that for any simple space-filling curve, there always exists a pair of close points in the plane, such that their distance along the space-filling curve is arbitrarily large. Energy consumption is a crucially important issue in battery-driven wireless sensor networks (WSNs). In most sensor networks, the sensors near the data collector (i.e. the sink) become drained more quickly than those elsewhere in the network since they are required to relay all of the data collected in the network to the sink. Therefore more balanced data paths to the sink should be established in order to extend the lifetime of the sensor network. Accordingly, a novel relay deployment scheme for WSNs based on the Voronoi diagram is proposed. The proposed scheme is applicable to both two-dimensional and three-dimensional network topologies and establishes effective routing paths that balance the traffic load within the sensor network and alleviate the burden on the sensors around the sink. Simulation results indicate that the number of relays deployed in the proposed scheme is similar to that deployed in the predetermined location scheme and is significantly less than that deployed in the minimum set cover scheme. Furthermore, the lifetime of the sensor network containing relay nodes deployed using the current scheme is longer than that achieved using either the predetermined location scheme or the minimum set cover scheme.