Mobile wireless sensor networks (MWSNs) are a relatively new type of WSN where the sensor nodes are mobile. Compared to static WSNs, MWSNs provide many advantages, but their mobility introduces the problem of frequent reauthentication. Several mobile node reauthentication schemes based on symmetric key cryptography have been proposed to efficiently handle frequent reauthentication. However, due to security weaknesses such as unconditional forwarding or low-compromise resilience, these schemes do not satisfy the security requirements of MWSNs. In this paper, we propose an energy-efficient and secure mobile node reauthentication scheme (ESMR) for MWSNs that satisfies the security requirements of MWSNs by addressing the security weaknesses of previous studies. ESMR prevents unconditional forwarding by allowing a foreign cluster head to authenticate mobile nodes, providing high-compromise resilience because it limits the use of cryptographic keys for different purposes. Security analysis shows that ESMR meets security requirements and can prevent relevant security attacks. Performance evaluation shows that ESMR is suitable for multi-hop communication environment, where the number of hops between the mobile node and cluster head is two or more. Specifically, ESMR requires less than 6% increased total energy consumption and 3% increased reauthentication latency compared with previous studies; hence, it introduces negligible performance overhead. Considering both performance and security aspects, ESMR also can be applied to single-hop communication environment.
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different clustering techniques, an energy efficientclustering procedure is proposed here. Large number of Sensor nodes distributed in a controlled area is assumed to have initial energy greater or equal to some upper threshold value. The location of the nodes is found from the location finding system, embedded in the Sensor nodes. The grouping procedure is as follows. Staring from one of the node k, check energy level of all the nodes within the transmission range of k (within one hop distance). The node with highest energy level with in visibility range of k which has residual energy greater than some upper threshold value is declared as Leader of the group. Other nodes located within the visibility range of that leader and with energy level greater than minimum threshold value can be the followers or members of that group. This forms first cluster in the clustering process. We would like to have minimum number of clusters. This can be achieved by non overlapping leaders thereby no two leaders are not in direct communication range to each other. Hence clusters are linked through a follower node which is within range of present cluster and new cluster we are going to form. We call that follower node that links two clusters as mediator node. Hence cluster leader selects one of farthest follower having residual energy above certain threshold value and calls it as mediator and mediator decides next cluster head which is within its transmission range and is neither leader nor a follower of any group. Nodes located within the visibility range of that leader and with energy level greater than minimum threshold value and neither leader nor a follower of any group becomes the followers of that group. The cluster head selects mediator node to continue grouping process. This clustering process is repeated until all nodes are included in groups (In some cases few nodes can become only followers but not within the range of any leader, that cannot participate in grouping because of its lower energy level, they are as good as dead). Any member with in that group has to forward information to its leader. Any such event identified by leader will pass on the information to server. The leader needs a buffer to collect the information from its members. Main server assures the association of individual members through group leader.
Various approaches to exploit the mobility of data collection methods for WSNs have been proposed. The classification of these approaches according to the characteristics of sink mobility, and wireless data transfer methods: Mobile Base Station (MBS): MBS is a mobile sink, which amend the location through transmission. The sensors data transmit to MBS without delay. Mobile Data Collector (MDC): MDC act as mobile sink which visits individually all sensors in the network. Sensor generated data buffered at source until the MDC visits and retrieves the information by single hop transmission. Rendezvous Solution: Hybrid solution of WSNs mobility, where sensor data is collected at designated point near the mobile devices. Then mobile devices downloaded the buffered data from appointed points [6, 7, 8].
The proposed scheme uses a nonce-values mechanism to detect imposters anywhere in the network. As discussed earlier, sensors are constantly in motion and thus, need to obtain encryption keys to the neighboring nodes in order to route their sensed data. Sensor nodes communicate with the sink, requesting encryption keys to neighboring nodes in their vicinity. The sink needs to authenticate each sensor node requesting encryption keys, and grant the encryption keys to only those nodes which are authenticated. The sink node uses a nonce-values mechanism to authenticate sensor nodes. At the time of installation, each legitimate node in the net-work is issued a unique nonce-value, known only to the node and the sink. When node g needs to communicate with the sink, it sends its nonce-value to the sink as part of the message. The sink receives the message and verifies whether the nonce-value sent by node g matches the nonce-value maintained by the sink for node g. If the nonce-values match, the sender node is assumed to be a legitimate node and the communication can proceed. The sink, then, generates and sends new encryption keys enabling node g and its neighboring nodes to communicate with each other. At the end of such a successful communication between the sink and the node g, the sink generates a new nonce-value for node g, stores it in its memory and sends a copy to node g. This new nonce-value will be used to authenticate the identity of node g in the next communication between the sink and node g. The next time when node g communicates with the sink, it sends this new nonce-value to the sink to verify its identity. If the nonce-value sent by node g does not match the nonce stored by the sink for node g, the sink identifies node g to be an imposter.
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The LEACH-Mobile operation is broken up into rounds. Each round is divided into two phases, namely setup phase and steady state phase. In setup phase clusters are organized and in steady state phase data are transferred to the base station. The vital concept in LEACH-Mobile protocol is to confirm whether a mobile node is capable of communicating with specific clusterhead within the time slot allotted in TDMA schedule. In LEACH algorithm, the clusterhead waits to gather the sensed data from non clusterhead nodes according to TDMA scheduling during the steady state phase. There is a slight modification in steady the state phase of the LEACH protocol to support mobility of nodes. The clusterhead in LEACH mobile first sends the req. message for data transmission to non clusterhead node for gathering the sensed data at each time slot. When the data sensed are sent by non clusterhead nodes, the clusterhead will make the time slot list of nodes from which the data is received according to the TDMA time slot at all the time when a frame ends. It also makes note of the nodes from which the data are not received in the time slot at the end of each frame. If the data is not received again from same node when the next frame ends, then it is considered to be removed from the cluster. The time slot of removed node is assigned to a newly arrived node. The sensor node which is removed is considered as node gone out of cluster region due to mobility. Then the TDMA is rescheduled and transmitted to all cluster members by the clusterhead. The node which moved away from cluster region or the node which didn’t get the req. message for data transmission from the clusterhead for two successive frames should join the new clusterhead. That node will broadcast cluster joint req. message. The clusterhead which receives cluster joint req. message transmits clusterhead advertisement message to that node as a reply. Depending on the signal strength of received clusterhead advertisement message, the node will decide to join the new cluster. After deciding which cluster to join, the node will inform the corresponding clusterhead. The new clusterhead will revise the cluster membership list and TDMA schedule, and then broadcast the new TDMA schedule to its cluster members. The newly joined member works according to the new schedule. Figure 1 shows the time line for a round of the LEACH Mobile Protocol . LEACH Mobile Protocol could be used in mobility centric wireless sensor network as it organizes the
A wireless sensor network (WSN) consists of a large number of spatially distributed sensor nodes which are cheap, low power, limited in memory, energy constrained due to their small size and work cooperatively to monitor physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. A sensor node performs three basic functions- sensing, processing and communication. Until wireless sensor networks became popular, the wired sensor networks were being used for the same purpose but due to high cost of installation, termination, maintenance, upgradation and infeasibility of wired sensor networks in hostile and remote locations, wireless sensor networks became a good alternative . Since the sensor nodes in a wireless sensor network are randomly deployed so the positions of the sensor nodes need not to be predetermined. Figure 1. shows a typical wireless sensor network.
Time synchronization of clocks in the sensor nodes for wireless sensor networks (WSNs) is a fundamental technology for most mission-critical applications. Most of past research in time synchronization for WSNs, however, has only focused on achieving some of the goals at a time, such as accuracy, energy consumption, completion time, etc., making these solutions less capable of adapting to different application requirements. In this paper, we propose a new time synchronization algorithm named MBATS (mobile beacon-based adaptive time synchronization) in which a mobile beacon is employed to move or fly over the sensor deployment area to complete time synchronization. Moreover, MBATS is designed so that the number of sensor nodes that are synchronized by one instance of time synchronization from the mobile beacon could vary dynamically to meet application requirements on accuracy, completion time and energy consumption, making the proposed MBATS algorithm highly adaptable to different application requirements. In addition to showing the advantage of the proposed MBATS algorithm on the adaptability of time synchronization as well as on some of the main metrics of synchronization over comparable schemes for WSNs, we also present the results of our study on comparing the performance of letting the mobile beacon traverse along a designing path versus follow a random path. Such a study is important since it would allow us to learn the performance gains that we can expect to achieve with extra control effort spent on designing the path over the effortless random path strategy. Such study could provide us with some clues on how to choose a suitable time synchronization strategy to better meet application requirements, which may not necessarily be the designed path strategy due to the tradeoff between cost and performance gains.
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The novelty of our framework can be utilized as a smart navigation system as shown in Fig. 7.1. Through TPEG (Transport Protocol Expert Group) , it is more generalized to offer real-time information to users (e.g., traffic service in GPS) Also, in power grids, the application necessity by using Internet or cellular networks has been observed for the better energy efficiency . Especially, we see that there already exist smart-phone applications such as PlugShare , which offer the location of charging stations. Our intention is to support charging price information with the location service. This allows PEV users to gain cost savings through economical station selections. Furthermore, the hidden behind of our framework is that the price-based decision is integrated to algorithms that autonomously lead an improvement both for station throughput and QoS for PEVs.
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In Energy-Efficient Data-Dissemination protocol, EEDD , virtual-grid-based two-level architecture is adopted to extend the lifetime of a sensor network. The 2 levels of the grid are: Coarse and Fine level. At Coarse level, only some necessary nodes are kept active only via a detection process and rest nodes goes to long term sleep. At fine level, each grid is divided into several sub-grids and working nodes in each sub-grid will alternatively stay active from short term sleep according to a schedule. Data dissemination strategies are characterized into three types, i.e. Target location aware, Target area aware and Target location unaware. In EEDD, query and data is forwarded using diagonal-first routing strategy but query and data dissemination path are normally different from each other. In target location aware strategy, as the target’s location is known, query can be forwarded using the diagonal-first routing strategy until the query reaches the grid head whose grid ID has the same vertical or horizon coordinate value as that of the source grid. In Target area aware strategy, query is flooded to all grid heads in the sub-area where the source resides. In Target location unaware strategy, query flooding is done throughout the field and it reaches all grid heads in the field and the nodes which have event in their sensing field sends the relevant data
ABSTRACT: Mobile Ad-hoc network is infrastructure less, spontaneous and multi-hop network, which consist of decentralized wireless mobile nodes. MANET uses temporary ad-hoc network topologies, that allowing people and devices to seamlessly connect to network in areas with no pre-existing communication infrastructure e.g. disaster recovery environments. And mobile ad-hoc network is a collection of nodes that are connected through each other with a wireless medium forming rapidly changing topologies. Routing in mobile ad-hoc network is a challenging term due to its dynamic changes in topologies. There are lots of trust models and routing protocol which are used in MANETs to get a security. Various trust schemes are used to provide integrity, confidentiality and availability in mobile ad-hoc network to gain the secure environment. In this paper, we will discuss characteristics, attacks and security in mobile ad- hoc network.
In most of the existing works, efforts have been made to develop MAC protocols that primarily conserve energy assuming static nodes. They failed to consider real life situations where some nodes are mobile hence this renders the existing schemes inefficient when it comes to mobility. However, for the purpose of this work Gauss Markov mobility model is adopted. This work will also use Clear Channel Assessment (CCA) and pack- ets back-off for channel negotiation and Low Power Listening (LPL) for low power communication and also mobility patterns for node awareness. This is with a view to check the status of the channel and report back if there is activity. The rest of this paper is organized as follows. In Section 2, we presented reviews of related works. We described the overview of a Mobility Conscious Medium Access Control (MAC) Scheme in Section 3. In Section 4, the model formulation is presented while Section 5 offers concluding thoughts and future work.
To overcome these drawbacks, we propose a Distributed detection scheme to identify and block mobile Malicious nodes by leveraging the Sequential Probability Ratio Test (SPRT). Our scheme is designed to quickly detect and revoke mobile malicious nodes in a fully distributed manner We leverage the intuition that immobile sensor nodes appear to be present around their neighbor nodes and communicate with them regularly. We describe how we embed this intuition into the SPRT so that neighbors can detect and block nodes that are silent unusually often. 2.NETWORK MODEL
The placement problem of relay nodes in flat architectures is considered in  ， ，  and . In  , the authors focus on placing a minimum number of relay nodes to ensure that the resulting network is connected. They consider a class of sensors, where the location of the sensor nodes are pre-determined, and modeled the problem based on the well known Steiner minimum tree with minimum number of Steiner points and bounded edge length  problem. They propose two approximation algorithms. In , the authors focus on maximizing the lifetime of a sensor network, under the constraint that each point in the sensing region is covered by at least one sensor node. In their model, any node can assume the role of a sensor node or a relay node. They propose an algorithm for finding the location of nodes, along with their roles, to achieve this objective. In , the authors address the placement problem of the sensor nodes, the relay nodes and the base stations, and propose a number of ILP formulations to achieve different objectives, such as: a) Minimizing the number of sensor nodes to be deployed while maintaining the coverage and the connectivity, b) minimizing the cost and the energy consumption, and c) maximizing the lifetime. In , the authors formulate the relay node placement problem, with the objective of maximizing the lifetime of the network, as a nonlinear program and propose an approximation algorithm. The general problem of finding an optimal placement of relay nodes is NP-hard, even finding approximate solutions is NP-hard in some cases .
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Wireless Sensor Networks (WSNs) are increasingly used in data-intensive applications such as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit all the data generated within an application’s lifetime to the base station despite the fact that sensor nodes have limited power supplies. We propose using low cost disposable mobile relays as doze nodes to reduce the energy consumption of data-intensive WSNs. Our approach differs from previous work in one main aspect. Mobile relay does not actively forward data from source node to base station but continues to listen to the source nodes and monitors the signal level around it. It gets active and forward the data from source node to base station, when there is a noticeable change in the signal level. In the sleeping time of the mobile relay, it does not consume more energy. Finally this technique produces an efficient energy optimization that can be integrated in the system that can be used in the data-intensive applications. We further conduct extensive simulations to examine the efficiency of our technique with varied network settings.
A network of wireless sensor nodes can be formed by densely deploying a large number of sensor nodes in a given sensing area, from where the sensed data from the various nodes are transported to a monitoring station (called as sink node or base station), often located far away from the sensing area. The transport of data from a source node to the monitoring station can be carried out by multi hop routing or flooding. By having more than one Base stations the average number of hops between data source sink pairs can get reduced. This will reduce the energy spent by a given sensor node for the purpose of relaying data from other nodes towards the base station, which, in turn, can potentially result in increased network lifetime as well as in larger amount of data delivered during the network lifetime. So the deployment of communication nodes as well as the multiple sink nodes is most important factors that affect the lifetime in wireless sensor network. Again, in this paper the focus is also on the issue related to reduce power consumption and increase network life time.
channel. The cost effective wireless communication plays a vital role in wireless sensor network because the installation of wireless sensor network is too easy and cost effective. At the beginning of the sensor network, the sensor node deployment is a major issue for researchers which means the suitable deployment strategy will give better sensor network. Such sensor node deployment can be done with random manner as well as deterministic manner. The sensor node thrown from the helicopter or air vehicle for military surveillance applications which is called random deployment. Each node doesn‟t know its own location and some of the node might not be dropped within the coverage area of the neighbor node. It requires mobility to reach the communication range of Abstract: Now a day’s, we are very much interested in smart Agricultural field. As per smart envision of agriculture, the sensors are playing a vital role to measure physical parameters of the field. To make a smart agricultural field, it requires multiple sensor node which should be deployed in stipulated places based on the internal characteristics of the node. In this paper, the deployment of nodes are mainly depends on to cover a deterministic area and utilize the minimum number of nodes to get maximum coverage of the field (‘K’ coverage). In this research analyzes, it has been analyzed an effective sensor nodes deployment Strategies to get ‘K’ coverage as well as analyzed geometric routing to get less energy consumption. The sensor nodes are deployed under two different conditions for getting maximum coverage by using minimum number of sensors of deterministic area. As well as it has been analyzed an energy efficient geometric routing by utilizing the geometric deployment. According to this research analyzes, the total number of intermediate hops to reach information from source to sink has been vastly varied between two different conditional deployments. And the distant based geometric routing energy consumption is also varied under two different geometric deployment strategies. As per research analysis the communication consumes more energy than sensing and processing of sensor node. So, if the number hops increases between source to sink then the energy consumption to transmit and receive the information is also getting increases.
Vitality utilization and system lifetime are the greatest significant highlights inside the design of the remote sensor organize. This watch blessing grouping based thoroughly steering for WSNs. Numerous grouping principally based conventions are homogeneous, which incorporates LEACH  PEGASIS . Cluster Heads gather insights by its people or vassal hubs, total and ahead to far flung situated Base Station. This strategy over- burdens the Cluster Head and it expends part of power. In Low Energy Adaptive Cluster Hierarchy, the Cluster Head is settled on occasionally, eat up unique vitality with the guide of picking a fresh out of the plastic new CH in each round. A hub develop to be CH in present day round on the possibility of likelihood p. Drain plays appropriately in homogenous system be that as it may, this convention isn't viewed as legitimate for heterogeneous systems as demonstrated in . In  maker offered some other grouping protocol(TLLEACH). This tradition delineates degree packing arrange for which performs enjoyably in articulations of insignificant essentialness affirmation of lattice. Here are 2 kinds of periods of Cluster Heads, type 1 Cluster Heads and degree Cluster Heads. Sort one Cluster Heads append with their contrasting part sensor center points. Bunch Heads at 2d degree make bunches from Cluster Heads of stage one. TL-LEACH plot is without a doubt extra control along these lines; the stack of the system at the sensors is all around shared which realizes apparently unending sensor organize. In PEGASIS  centers shape a progression to trade estimations from source to sink. In chain improvement system each center point connect with next center point. The chain course of action system require general data of sensor center points, along
panels. Therefore, a key challenge faced by data- intensive WSNs is to minimize the energy consumption of sensor nodes so that all the data generated within the lifetime of the application can be transmitted to the base station. Several different approaches have been proposed to significantly reduce the energy cost of WSNs by using the mobility of nodes. A robotic unit may move around the network and collect data from static nodes through one-hop or multi-hop transmissions. The mobile node may serve as the base station or a “data mule” that transports data between static nodes and the base station . Mobile nodes may also be used as relays that forward data from source nodes to the base station. Several movement strategies for mobile relays. Secret key generation (SKG) from shared randomness at two remote locations and has recently been extended to unauthenticated channels. SKG techniques have also been incorporated in protocols that are resilient to spoofing, tampering and man-in the-middle active attacks. Still, such key generation techniques are not entirely robust against active adversaries, particularly during the advantage distillation phase. Denial of service attacks in the form of jamming are a known vulnerability of SKG systems; in, it was demonstrated that when increasing the jamming power, the reconciliation rate normalized to the rate of the SKG increases sharply and the SKG process can in essence be brought to a halt. As SKG techniques are currently being considered for applications such as the Internet of things (IoT) , the study of appropriate counter- jamming approaches is timely. WSN’s have been deployed in a variety of data intensive applications including micro-climate and habitat monitoring , precision agriculture, and audio/video surveillance. A moderate-size WSN can gather up to 1 Gb/year from a biological habitat . Due to the limited storage capacity of sensor nodes, most data must be transmitted to the base station for archiving and analysis. However, sensor nodes must operate on limited power supplies such as batteries or small solar panels. Therefore, a key challenge faced by data-
In an environment where source nodes are close to each other, and considerable redundancy exists in the sensed data, the source nodes generate a large amount of traffic on the wireless channel, which not only wastes the scarce wireless bandwidth, but also consumes a lot of battery energy. Instead of each source node sending sensed data to the sink node, as typically occurs in client/server-based computing, this paper proposes a mobile agent paradigm for data processing/aggregating/ concatenating in a planar sensor architecture. The proposed architecture is called MAWSN (mobile agent based wireless sensor network). For a given set of parameters, we derive the conditions under which MAWSN exhibits better performances than the
MU-MIMO can greatly speed up data collection time and reduce the overall latency. Another application scenario emerges in disaster rescue. For example, to combat forest fire, sensor nodes are usually deployed densely to monitor the situation. These applications usually involve hundreds of readings in a short period (a large amount of data) and are risky for human being to manually collect sensed data. A mobile collector equipped with multiple antennas overcomes these difficulties by reducing data collection latency and reaching hazard regions not accessible by human being. Although employing mobility may elongate the moving time, data collection time would become dominant or at least comparable to moving time for many high-rate or densely deployed sensing applications. In addition, using the mobile data collector can successfully obtain data even from disconnected regions and guarantee that all of the generated data are collected.