Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance. <s> BIB001 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Wireless mobile ad hoc networks (MANETs) have the potential for use in important application environments, such as remote environmental monitoring, where energy resources are limited. Efficient power management is necessary to allow these networks to operate over a long period of time. One of the key factors affecting the design of power management mechanisms is the routing protocol in use within the network. In this paper, we investigate the Message ferrying (MF) routing paradigm as a means to save energy while trading off data delivery delay. In MF, special nodes called ferries move around the deployment area to deliver messages for nodes. While this routing paradigm has been developed mainly to deliver messages in partitioned networks, here we explore its use in a connected MANET. The reliance on the movement of ferries to deliver messages increases the delivery delay if a network is not partitioned. However, delegating message delivery to ferries provides the opportunity for nodes to save energy by aggressively disabling their radios when ferries are far away. To exploit this feature, we present a power management framework, in which nodes switch their power management modes based on knowledge of ferry location. We evaluate the performance of our scheme using ns-2 simulations and compare it with a multihop routing protocol, dynamic source routing (DSR). Our simulation results show that MF achieves energy savings as high as 95% compared to DSR without power management and still delivers more than 98% of messages. In contrast, a power-managed DSR delivers many fewer messages than MF to achieve similar energy savings. In the scenario of heavy traffic load, the power-managed DSR delivers less than 20% of messages. MF also shows robust performance for highly mobile nodes, while the performance of DSR suffers significantly. Thus, delay tolerant applications can use MF rather than a multihop routing protocol to save energy efficiently when both routing approaches are available. <s> BIB002 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> In addition to the miniaturized sensor nodes, wireless sensor and actor networks (WSANs) employ significantly more capable actor nodes that can perform application specific actions to deal with events detected and reported by the sensors. Since these actions can be taken at any spot within the monitored area, the actors should be carefully placed in order to provide maximal coverage. Moreover, the actors often coordinate among themselves in order to arbitrate tasks and thus inter-actor connectivity is usually a requirement. In this paper, we propose a distributed actor positioning algorithm that maximizes the coverage of actors without violating the connectivity requirement. The approach applies repelling forces between neighboring actors, similar to molecular particles in Physics, in order to spread them in the region. However, the movement of each actor is restricted in order to maintain the connectivity of the inter-actor network. The performance of the approach is validated through simulations. <s> BIB003 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> The effectiveness of wireless sensor networks (WSNs) deployed in search and rescue, battlefield reconnaissance, surveillance, and other applications depends on inter-node interaction and maintaining network connectivity. While connectivity can be provisioned at startup, then sustained through careful coordination when nodes move, the network can be partitioned if a node suddenly fails. This paper presents recovery through inward motion (RIM), a distributed algorithm to efficiently restore network connectivity after a node failure. Instead of performing a network-wide analysis to assess the impact of the node failure and set a course of action, RIM triggers a local recovery process by relocating the neighbors of the lost node. RIM minimizes messaging overhead and reduces the distance that individual nodes travel during the recovery. Simulations validate RIM's performance. <s> BIB004 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> The major challenge in designing wireless sensor networks (WSNs) is the support of the functional, such as data latency, and the non-functional, such as data integrity, requirements while coping with the computation, energy and communication constraints. Careful node placement can be a very effective optimization means for achieving the desired design goals. In this paper, we report on the current state of the research on optimized node placement in WSNs. We highlight the issues, identify the various objectives and enumerate the different models and formulations. We categorize the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively. We further classify the published techniques based on the role that the node plays in the network and the primary performance objective considered. The paper also highlights open problems in this area of research. <s> BIB005 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> In Wireless Sensor and Actor Networks (WSANs) a connected interactor topology is desirable in order for the deployed actors to work collaboratively. If a critical actor fails causing the inter-actor network to get partitioned into disjoint segments, the other actors close to the faulty node often exploit their mobility to autonomously restore the lost inter-actor connectivity. However, such a solution focuses on resource efficiency and assumes no constraints on the mobility of actors which can be impractical in the real scenarios. In addition, since actors need to carry out tasks to meet the application level requirements, unconstrained movement of actor(s) to restore interactor connectivity can cause a major failure at the application level. This paper presents C2AM; a recovery algorithm that factors in application level constraints on actor's mobility while restoring the network connectivity. In addition to considering physical level requirements, C2AM accounts for application level concerns as well in order to avoid major disruptions to ongoing missions. Simulation results have validated the effectiveness of the algorithm in maintaining both objectives. <s> BIB006 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Recent years have witnessed a growing interest in the applications of wireless sensor networks (WSNs). In some of these applications, such as search and rescue and battlefield reconnaissance, a set of mobile nodes is deployed in order to collectively survey an area of interest and/or perform specific surveillance tasks. Such collaboration among the sensors requires internode interaction and thus maintaining network connectivity is critical to the effectiveness of WSNs. While connectivity can be provisioned at startup time and then sustained through careful coordination when nodes move, a sudden failure of a node poses a challenge since the network may get partitioned. This paper presents RIM; a distributed algorithm for Recovery through Inward Motion. RIM strives to efficiently restore the network connectivity after a node failure. Instead of performing a networkwide analysis to assess the impact of the node failure and orchestrate a course of action, RIM triggers a local recovery process by relocating the neighbors of the lost node. In addition to minimizing the messaging overhead, RIM opts to reduce the distance that the individual nodes have to travel during the recovery. The correctness of the RIM algorithm is proven and the incurred overhead is analyzed. The performance of RIM is validated through simulation experiments. <s> BIB007 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. A failure of a sensor leads to loss of connectivity and may cause partitioning of the network into disjoint segments. A number of approaches have been recently proposed that pursue node relocation in order to restore connectivity. However, these approaches tend to ignore the possible loss of coverage in some areas, either due to the failure itself or due to the connectivity-limited focus of the recovery. This paper fills this gap by addressing the connectivity and coverage concerns in an integrated manner. A novel coverage conscious connectivity restoration (C^3R) algorithm is presented. C^3R involves one or multiple neighbors of the failed node to recover from the failure. Each neighbor temporarily relocates to substitute the failed node, one at a time, and then returns back to its original location. This leads to intermittent connectivity and monitoring of all the originally covered spots. This paper also presents energy-centric optimized recovery algorithm (ECR), an optimized version of C^3R that is geared for energy efficiency. Both C^3R and ECR are validated through simulation. The simulation results confirm the effectiveness of these approaches. <s> BIB008 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Failure of one or multiple critical nodes may partition wireless sensor networks into disjoint segments, and thus brings negative effect on the applications. We propose DCRS, a Distributed Connectivity Restoration Strategy to tolerate the failure of one critical node. Because of the energy restriction of sensor nodes, the energy overhead of the recovery process should be minimized to extend the lifetime of the network. To achieve it, we first design a novel algorithm to identify 2-critical nodes only relying on the positional information of 1-hop neighbors and some 2-hop neighbors, and then we present the criteria to select an appropriate backup for each critical node. Finally, we improve the cascaded node movement algorithm by determining whether a node can move to another non-adjacent node directly or not to reduce the number of nodes moved. The effectiveness of DCRS is validated through extensive simulation experiments. <s> BIB009 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Recent technological advances and an increasing thrust toward automation have resulted in a rapid adoption of Wireless Sensor Networks (WSNs) as the de facto choice in monitoring and surveillance applications. Their low cost, versatility and ability to operate around the clock in inhospitable terrains without endangering human life make WSNs ideal for applications like space exploration, environmental monitoring and combat zone surveillance. In these applications WSNs are to operate autonomously for prolonged durations; thus self-healing from failures becomes a requirement to ensure robustness through sustained network connectivity. The paucity of resources makes node repositioning the method of choice to recover from failures that partition the network into numerous disjoint segments. In this paper we present a Geometric Skeleton based Reconnection approach (GSR) that exploits the shape of the deployment area in order to restore connectivity to a partitioned WSN in a distributed manner. GSR decomposes the deployment area into its corresponding two dimensional skeleton outline, along which mobile relays are populated by the surviving disjoint segments to reestablish connectivity. The performance of GSR is validated through mathematical analysis and simulation. <s> BIB010 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Since low-cost devices have been acclaimed in wireless sensor networks (WSNs), their applications in harsh surroundings, i.e., combat field reconnaissance, border protection, space exploration, etc. have become very common nowadays. Due to an unhealthy and exposed environment in which the proposed network has to be operated, sometimes result in damage of large-scale backbone actor nodes that causes connected network split into multiple disjoint segments. Placement of relay nodes (RNs) is the only way to connect the partitioned network, but the higher cost of RNs then becomes an addressable issue of their placement. A new solution based on concentric Fermat points towards the center of deployment called restore relay lost connectivity using concentric fermat points (RRLC-CFP) for RNs’ placements is proposed. The simulation results confirm the potency of our proposed solution. <s> BIB011 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Nowadays communication by using mobile vehicle getting popularity and user experience map services like Google maps to reach at the destination. It is also clear that map services user have to reveal important information like GPS coordinates or some other personal information during this process. According to current mix-zones privacy techniques, it is revealed that, these methods are much generalized and cannot be applied to secure map services users. In this paper, we have given a novel MMLPP technique, which is specifically constructed for map services users over road network. According to this, mobile vehicle’s users to ask a path among two end points defined on the map, however not to disclose secret location and query statistics. The main concept is to place end points in such a way that, they must be near to each other, alternatively: 1) Actual statistics defined inside these end points (like GPS coordinates) must not be altered i.e. location privacy ensured. 2) The path reverted by the map services change a little, i.e. service usability must be ensured. We can re-iterate in such a way that, firstly , a mobile client extract point of interest , that is close to original end points, and then finally consider two POIs considered as shifted end points which satisfy the condition of Geo-Indistinguishability. We analyzed MMLPP technique for road network application for GTMobiSim on multiple scales of map services and experiments are conducted using real traces. It has been revealed that MMLPP has outperformed all other techniques and shows balance among location privacy and service usability. <s> BIB012 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Wireless sensor networks are envisioned to play a very important role in the Internet of Things in near future and therefore the challenges associated with wireless sensor networks have attracted researchers from all around the globe. A common issue which is well studied is how to restore network connectivity in case of failure of single or multiple nodes. Energy being a scarce resource in sensor networks drives all the proposed solutions to connectivity restoration to be energy efficient. In this paper we introduce an intelligent on-demand connectivity restoration technique for wireless sensor networks to address the connectivity restoration problem, where nodes utilize their transmission range to ensure the connectivity and the replacement of failed nodes with their redundant nodes. The proposed technique helps us to keep track of system topology and can respond to node failures effectively. Thus our system can better handle the issue of node failure by introducing less overhead on sensor node, more efficient energy utilization, better coverage, and connectivity without moving the sensor nodes. <s> BIB013 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> B. On-Demand Relocation <s> Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20–90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization. <s> BIB014
The effectual network for relocation/repositioning emerges as the objective of this category. The effectiveness at the outset of network application lessens essentially for the faulty sensor node. Besides, the essence of the output can also be affected by several fluctuations in the requirements of the application. Moreover, in other cases, it is assumed that sensor nodes should move methodically for keeping the network arrangement efficient. During sensor relocation, rather than repositioning the sensors at the deployment stage, a definite number of sensors are alternatively moved by on-demand relocation to reinforce the specific performance metrics  BIB013 . The existing connectivity restoration techniques BIB007 BIB012 mainly consist of two steps i.e. failure detection and recovery. Failure detection is used to discover a node or a component failure. It can be classified into single and collaborative diagnosis depending on the number of nodes involved. Single diagnosis can further be categorized in to self and non-selfdiagnosis. In self-diagnosis, a node can only determine some of its component failures. For instance, some of the faults can be determined by the actor node  itself such as loss of actor action ability, remaining battery power, communication faults, etc. Non-self-diagnosis involves one of the neighbor actor nodes detecting the failed actor based on the heartbeat messages that nodes exchange with each other as part of the network operation. For example, approaches like PADRA BIB007  involve one of the neighbors detecting the failure. On the other hand, collaborative diagnosis employs more than one node to determine the failure of a particular actor. It can further be categorized based on the number of nodes involved. Whereas, the MPADRA employs two actor nodes and RIM BIB004 involves all the neighbors in order to detect the failure. In  authors have addressed the substitution of failed node by putting forward a mechanism called Cascaded Movement, in which, nodes use a recurrent substitution towards sensor close to another redundant one. Fig. 5 shows the Cascaded Movement where the sensor s1 characterizes redundant sensor. Moreover, sensors s4 and s1 are faulty and redundant sensors. The sensor s3 after finding s4 failure will be searching for the redundant sensor. Using the sensor s2, as per presumption, the sensor s3 discovers s1 that is the redundant node, and the failed node will be superseded by the cascaded movement of sensors s3, s1, and s2, latterly (movements of s1-s2, s2-s3, and s3-s4 occur instantaneously). Correspondingly, connectivity is taken into account by the further mechanism whereas demonstrated in BIB001 BIB005 , one technique prefers on keeping up the 2-degree connectivity even under node or link failure depending on the mobility of a subset of the sensor node. While the relocation time can be reduced by using cascaded movement; due to disarrangement of the redundant sensor node, the reviewers are incapable of reducing the time of finding out the redundant sensor node. In the meantime, post-deployment coverage and connectivity were studied by BIB003 and  . With Connected Coverage and Latency-aware Actor placement (COCOLA), architecture of the network is designed in such a way that is www.ijacsa.thesai.org ranked orderly. Following the aforementioned methodology, high-tiered sensors are relocated, progressively, in order to maximize the coverage but without extension to the data route of a sensor on 1 st tier. Purposively, this is for maintaining the desirable bound on latency of data BIB002 . In spite of that, the influence exerted by faulty sensor note remains unhandled by both COCOLA  and C 2 AP BIB006 . In BIB004 authors have proposed RIM (Recovery through Inward Motion) and NN (Nearest Neighbor) techniques; both of which are conformed to the avid heuristics. As a result of failure of node in NN, it will proceed to progress towards Failure Neighbor Node (FNN) being one in close proximity to F position is for restoring the connectivity failure immediately. As a result, a movement of the highly adjacent node among the FNN neighbors takes place, which, subsequently, will position as a replacement of FNN and ultimately the neighboring FNN responds to its elimination. For the purpose of establishing network connectivity, RIM as illustrated in Fig. 7 , a procedure is established via inner movement that acquires a prominent method of displaying the inward direction of neighbors of F node to the particular point after the failure occurs in order to set up an interconnection amongst the nodes. This phenomenon is determined by neighbor nodes instantaneously influenced by the failure of F node, and subsequently, once approaching one another, internode connectivity will be restored aimed to its former place before the breakdown. The repositioning is done by recursion to any sensor node that is not relevant to the neighbor node's movement, supposedly, nodes that have moved out towards the damaged one. NN consequently ceases its operation either due to the absence of the neighbor node for a dead sensor (approaching the network borderline), or else, after every node's movement in the network. Provided that RIM enforces the list of one-hop neighbor, the procedure in NN technique, sequentially, postulates that each node should be aware for its two-hop neighbor, subsequently, it can empower to discover the neighbor in the close proximity earlier than the breakdown of F sensor node. In this scenario, there is no relevance of NN and RIM with the aftermath that probably appears in restoring the connectivity of the enclosed network. This cascaded repositioning could be prevented by C 3 R procedure, also known as Coverage-Conscious Connectivity Restoration BIB008 . Despite the fact that if replacing the neighbor node by other is restore the connectivity among nodes, it clearly modifies the coverage hole in the sensor's area partially in point of fact, either in the network's interior part or mobile sensor relocation along with border to tackle efficiently with connectivity and coverage from the sensor node's breakdown. The sensors, instead of network topology reconfiguration, work back and forth to substitute failed sensor so as to provide sporadic recovery rather than a permanent one. Fig. 8 illustrates how C 3 R operates based on the patterned network. Moreover, it can be seen that the capacity of neighbor node's connectivity with n9 sensor node will be diminished and rapidly goes through the internal movement after detecting the defect of n9. This is carried out to design a pattern that overcomes the breakdown of the node. In addition, as a matter of fact, that n11 sensor node is lying in close range to n9, it swifts speedily than other neighboring nodes towards n9. Besides, n11 serves as a coordinating node as represented in Fig. 8(b) . In this scenario, several nodes will assemble the scheduled data from n11 and go back to the respective places excluding n2 node because it serves as an initiating node organized for restoration and acquire a strong overlapping coverage as indicated in Fig. 8(c) . Fig. 8(d to g) , furthermore, depict to and fro motion of various neighbors of n9 sensor node successively for the recovery of the network from failure of www.ijacsa.thesai.org node n9. This approach noticeably unfolds numerous periodic changes in the inter-connection and possibly can inflict a considerable amount of overhead. It is therefore unambiguous about this mechanism being transient in terms of anticipating the efficiency of the spare nodes. C 3 R, furthermore, handles only a single failure individually and doesn't cope with the simultaneous and multiple node failures. The DCRS (Distributed Connectivity Restoration Strategy) BIB003 has been put forward to deal with the faulty critical node. The mechanism is illustrated in Fig. 9 . Due to energy limitation of the sensor nodes, the energy overhead of the repairing process should be reduced to prolong the network's lifespan BIB014 . For implementation, authors initially designed an algorithm for the identification of two critical nodes that merely rely on the factual data of position of the one-hop and a few two-hop neighbors, and a methodology of choosing the pertinent backup has been introduced for every critical node. A distributed Resource Constrained Recovery (RCR) technique illustrates the procedure of restoring the network connectivity by the strategic relocation of nodes BIB009 . The GSR is proposed that exploits form of deployment region to facilitate the restoration of the connectivity in the partitioned WSN in distributed pattern. This approach disintegrates the area of deployment into the equivalent 2D skeleton outline, with a mobile transmission that is occupied by the survival of the disjoint parts to recover the connectivity BIB010 . The RRLC-CFP (Restore Relay Lost Connectivity using Concentric Fermat Points) algorithm is proposed for the network restoration which is based on the transmitted node's placement. In this technique network partitioned due to multinode failure, the problem is considered and solved by a novel approach named Concentric Fermat Points. This approach is used in data transmission for the reduction of the distance of data transmission among the nodes in order to boost the life time of the network BIB011 . So in this section, different connectivity restoration techniques were presented based on on-demand relocation. In the next section, we present a comparison of all the existing techniques presented in this paper and summarize all the information in tabular form. Fig. 10 shows the list of on-demand repositioning techniques discussed in this paper.