A Density Control Algorithm for Surveillance Sensor Networks

Wireless Sensor Networks are becoming increasingly popular in everyday life since they offer a variety of network structures for developing cutting-edge real-time applications. Wireless sensor devices have high energy consumption limitations, so it is necessary to handle excessive energy consumption by malicious nodes properly to improve network performance. Even though numerous studies have been conducted to increase the dependability of routing in WSNs, the existing routing strategies do not meet the required security constraints by using intelligent methods to protect the sensor nodes from malicious attack. To overcome this challenge a novel Trust Aware Clustering based Secure Routing Techniques (TAC-SRT) has been proposed to minimize the overall energy consumption, improved security to nodes and to maximize the network lifetime. The proposed method is carried out in three phases. In the first phase, the cluster head is selected by using K mean clustering. In the second phase, the trust value of each node is evaluated by using Mamdani fuzzy inference rule. In the third phase, the Tversky similarity index is used to find the normal or malicious node and establishes the shortest route. The Fully Homomorphic Elliptic Curve Cryptography technique is then used to perform secure data transmission. The effectiveness of the proposed strategy is examined using several parameters, such as the lifetime of the network, data confidentiality, active nodes, and energy consumption. The proposed technique improves the network lifetime by 23.01%, 17.4%, and 13.2% better than MOSFA, SecDL, and CAR-MOSOA respectively. Finally, the proposed method demonstrated superior performance in terms of delay, throughput, encryption time, network lifetime, and packet delivery ratio compared with existing techniques.

Wireless Sensor Networks demand energy conserving protocols at all layers of protocol stack in order to achieve increased network lifetime. In this work, we propose a node scheduling protocol for sensor networks that exploits the battery charge recovery property and node level redundancy with respect to sensing, in order to maximize the network lifetime. The protocol forms a virtual grid and periodically elects the active node in a given grid. The primary battery sources of the remaining redundant nodes are completely switched off. The periodic election of active nodes ensures that every redundant node recovers its battery charges during their inactive period. We compare the performance of our protocol with baseline approaches like Strict Alternation, Periodic Alternation which are redundancy aware and also with the standard IEEE 802.11 and T-MAC protocols. The simulation results reveal that our protocol achieves network lifetime as high as 20% to 40% than the other schemes, with un-interrupted sensing and reporting of the events to sink.

In wireless sensor networks, one of main design challenges is to save severely constrained energy resources and obtain a long system lifetime. Low cost of sensors enables us to initially deploy a large number of sensor nodes in a very high density. Thus, a potential approach to solve a lifetime problem arises. That is to schedule sensor nodes work alternatively by configuring some of them an off-duty status that has lower energy consumption than normal on-duty one. In a single wireless sensor network, sensors assume two main functionalities: sensing and communication. Minimizing energy consumption in a highly dense wireless sensor network needs to maximize off-duty nodes in both domains. Most of previous work addressed only one kind of redundancy: sensing or communication alone. Based on our previous conclusion: the communication is twice sensing range is sufficient condition and tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes, if original network topology (consisting of all deployed nodes) is connected, we study a node status configuration strategy in a tightly integrated two-phase mode for those sensor networks where communication is more than twice sensing by comparing this strategy with other two in term of off-node number, experimental results show that it leads to more off-duty nodes in double domains without incurring reduction of off-duty nodes in a single domain.

Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered & transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power & limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation & fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.

ODT: Optimal deadline-based trajectory for mobile sinks in WSN: A decision tree and dynamic programming approach

One of the major concerns in wireless sensor networks is improving the network lifetime. In this paper, we propose a new energy provisioning model and investigate the relationship between the network lifetime and the network density by using both simulations and analytic models. We show that the average amount of energy required to report an event to the sink decreases when sensor network density increases. Moreover, we demonstrate that the speed of increasing the network lifetime goes up faster than that of the network density. Based on this result, we prove that nodes must be deployed at the same time and not over different deployment phases.

Area coverage protocols aim at turning off redundant sensor nodes while ensuring full coverage of the area by the remaining active nodes. Connectivity of the active nodes subset must also be provided so that monitoring reports can reach the sink stations. Existing solutions hardly address these two issues as a unified one and very few are robust to non ideal physical conditions. In this paper, we propose a deep analysis and some enhancements of a localized algorithm for area coverage, based on Surface Coverage Relays (SCR) and able to build connected active nodes sets that fully cover the area. We first enhanced the critical phase of our protocol (the relay selection) and show that the number of active nodes can be drastically reduced. We then raise the issue of the robustness of the protocol once a realistic physical layer is simulated. Our algorithm proved itself to be an interesting solution as it remained able to still ensure high coverage level under realistic physical layer conditions. We also added the possibility to finely tune the overall proportion of active nodes through a new parameter used during local relay selection phases.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

In a wireless sensor network, a large number of small-size cheap sensor nodes are scattered, and have limited resources such as battery power, data processing capability and memory capacity. They should show fault-tolerance, energy efficiency, and topological adaptation to the change of external environment, therefore, an effective routing algorithm is essential. In general, if a network is large in scale, multi-hop transmission is more energy efficient than direct transmission. In case a large number of nodes are deployed densely within a fixed 1-hop communication range, however, many nodes participate in data transmission, multi-hop transmission consumes much more energy in such a case. Thus, this study proposes a routing algorithm that solves above problems at the same time in a dense and large scale sensor network. The result of experiment showed that routing algorithm proposed in this study has reasonable fault-tolerance, energy efficiency, and offers its adaptability to topological changes in a dense and large scale sensor network.

A wireless sensor network (WSN) comprised of a plurality of sensor nodes powered by their internal batteries and one or more base stations. Efficient utilization of the energy resources leads to prolonged network lifetime. In this study, the effects of limiting the total hop count within the network on network lifetime is investigated by using a mixed integer programming (MIP) framework. Our results show that when the hop count of network is limited by minimum hop count, lifetime of network can decrease by %40 of the lifetime obtained without any constraint on hop count. If hop count limit of network is increased by %10, lifetime of network can reach %98 of maximum lifetime obtained without any restrictions on hop count.

Robustness, fault tolerance, and a long lifetime are key requirements of sensor networks used in real-world applications. Dense sensor networks with high sensor redundancy offer the possibility of redundant sensing and low duty-cycle operation at the same time; thus, the required robust sensing services can be provided along with an elongated lifetime. In this paper, the controlled greedy sleep algorithm is analyzed. With low local communication overhead, the proposed algorithm is able to solve the k -coverage sensing problem while it effectively preserves energy in the network. In addition, it can adapt to dynamic changes in the network, such as node failures. The quality of service (networkwide k-coverage) is guaranteed independently of communication errors in the network (as long as it is physically possible); message losses affect only the network lifetime. Node failures may cause a temporary decrease in the coverage service. The robustness of the algorithm is proven, and its behavior is illustrated by simulation examples.

Aiming at the transmission of heterogeneous data in heterogeneous networks, a topology optimization algorithm of heterogeneous wireless sensor networks based on local tree reconstruction is proposed, which can achieve better data transmission in the heterogeneous networks. First, the algorithm divides the nodes of the network into different layers by their hops and chooses different numbers of nodes as relay nodes in different layers. Second, the nodes are set with different initial energies in different layers. Because the packets of different nodes have different sizes, we adopt the corresponding data aggregation coefficients according to the actual data requirements of the network in data transmission. Finally, the lifetime of the network is prolonged by real-time updating of the topology of the tree during the data transmission. The simulations indicate that after the aforementioned three steps, the proposed algorithm prolongs the lifetime of the heterogeneous networks and improves the nodes utilization effectively.

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Maximization of lifetime of wireless sensor network (WSN) is an emerging area of research in present scenario. Many authors are performing their research work, so that they could achieve lower energy consumption for the sensor network which leads to increase in lifetime of network. Our work focuses on sink mobility in a fixed trajectory within a wireless sensor network while sensed data are required to be collected. We divide the whole sensor network into two different regions as direct communication area (DCA) and multi-hop communication area (MCA). Sensors node that lies in DCA is at one hop count distance from the sink node trajectory while sensors within MCA are at more than one hop count from the trajectory. We considered all the sensors that are within DCA as subsink nodes. During sink mobility, whenever a subsink is closer to the mobile sink node, then it starts transmitting its data to the sink node. But those sensors that are within MCA needs to search an appropriate sink node for sending its sensed data to the subsink so that on further stage, that subsink could provide those data to the sink node whenever it is in nearest proximity of that subsink. Basically, a sink node is the ultimate destination for the data while a subsink node acts as a relay node for the nodes that are within MCA. So our work is to find out the appropriate subsink node from a given set of subsinks. For doing so, we used location-aided routing on global positioning system (GPS)-enabled sensors and sink node. Our work is validated through simulation experiments using NS-2.

The lifetime of wireless sensor networks (WSNs) are considered one of the most challenges that face the topology control of WSNs. Topology control of WSNs is a technique to optimize the connections between nodes to reduce the interference between them, save energy and extend network lifetime. In this paper proposed an algorithm based on Whale Optimization Algorithm (WOA) called WOTC, the paper provides a discrete version of the WOA, where the position of each Whale is calculate and represented in a binary format. The proposed fitness function is designed to consider two main target; a minimization in numbers of active nodes, and low energy consumption within these nodes to overcome challenges that face topology control to prolong the WSNs lifetime, the simulations were carried out using Attaraya a simulator. Consequently, the results showed that the final topology obtained by WOTC is better than A3 topology depending on the number of neighbors and their energies for active nodes, use a graph traversal function to ensure that all nodes which selected in network are covered in the best topology selection.