Secure coverage-preserving node scheduling scheme using energy prediction for wireless sensor networks

With the fast development of the micro-electro-mechanical systems(MEMS),wireless sensor networks(WSNs)have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

Expected k-coverage in wireless sensor networks

We are concerned with wireless sensor networks where n sensors are independently and uniformly distributed at random in a finite plane. Events that are within a fixed distance from some sensor are assumed to be detectable and the sensor is said to cover that point. In this paper, we have formulated an exact mathematical expression for the expected area that can be covered by at least k out of n sensors. Our results are important in predicting the degree of coverage a sensor network may provide and in determining related parameters (sensory range, number of sensors, etc.) for a desired level of coverage. We demonstrate the utility of our results by presenting a node scheduling scheme that conserves energy while retaining network coverage. Additional simulation results have confirmed the accuracy of our analysis.     

An energy‐efficient,scalable and collision‐free MAC layer protocol for wireless sensor networks

Wide range of applications such as disaster management, military and security have fueled the interest in sensor networks during the past few years. Sensors are typically capable of wireless communication and are significantly constrained in the amount of available resources such as energy, storage and computation. Such constraints make the design and operation of sensor networks considerably different from contemporary wireless networks, and necessitate the development of resource conscious protocols and management techniques. In this paper, we present an energy‐efficient, scalable and collision‐free MAC layer protocol for sensor networks. The approach promotes time‐based arbitration of medium access to limit signal interference among the transmission of sensors. Transmission and reception time slots are prescheduled to allow sensors to turn their radio circuitry off when not engaged. In addition, energy consumption due to active to sleep mode transitions is minimized through the assignment of contiguous transmission/reception slots to each sensor. Scalability of the approach is supported through grouping of sensors into clusters. We describe an optimization algorithm for energy conscious scheduling of time slots that prevents intra‐cluster collisions and eliminates packet drop due to buffer size limitations. In addition, we also propose an arbitration scheme that prevents collisions among the transmission of sensors in different clusters. The impact of our approach on the network performance is qualified through simulation.. Copyright © 2004 John Wiley & Sons, Ltd.     

EOMR: An Energy-Efficient Optimal Multi-path Routing Protocol to Improve QoS in Wireless Sensor Network for IoT Applications

The wireless sensor network based IoT applications mainly suffers from end to end delay, loss of packets during transmission, reduced lifetime of sensor nodes due to loss of energy. To address these challenges, we need to design an efficient routing protocol that not only improves the network performance but also enhances the Quality of Service. In this paper, we design an energy-efficient routing protocol for wireless sensor network based IoT application having unfairness in the network with high traffic load. The proposed protocol considers three-factor to select the optimal path, i.e., lifetime, reliability, and the traffic intensity at the next-hop node. Rigorous simulation has been performed using NS-2. Also, the performance of the proposed protocol is compared with other contemporary protocols. The results show that the proposed protocol performs better concerning energy saving, packet delivery ratio, end-to-end delay, and network lifetime compared to other protocols.

     

Looking for network functionalities’ extension by avoiding energy-compromised hotspots in wireless sensor networks

The vast literature on the wireless sensor research community contains many valuable proposals for managing energy consumption, the most important factor that determines sensors’ lifetime. Interesting researches have been facing this requirement by focusing on the extension of the entire network lifetime: either by switching between node states (active, sleep) or by using energy-efficient routing. We argue that a better extension of the network lifetime can be obtained if an efficient combination of management mechanisms can be performed at the energy of each single sensor and at the load distribution over the network. Considering these two accuracy levels (i.e., node and network), this paper presents a new approach that uses cost functions to choose energy-efficient routes. In particular, by making different energy considerations at a node level, our approach distributes routing load, avoiding, thus, energy-compromised hotspots that may cause network disconnections. The proposed cost functions have completely decentralized and adaptive behavior and take into consideration the end-to-end energy consumption, the remaining energy of nodes, and the number of transmissions a node can make before its energy depletion. Our simulation results show that, though slightly increasing path lengths from sensor to sink nodes, some proposed cost functions (1) improve significantly the network lifetime for different neighborhood density degrees, while (2) preserving network connectivity for a longer period of time.     

一种基于冗余度的无线传感器网络节能策略

在无线传感器网络中,传感节点由于采用电池供电,因此寿命有限。如何有效节省传感器节点的能量,延长网络的使用寿命,一直是广泛研究的焦点。文章提出一种适用于高冗余度布置的无线传感器网络结构中,节省传感器节点能量消耗的方法-接续调度法。该方法通过协调点对小区域内节点的调度,使区域内节点依次分时段工作。通过这种接续调度,避免了节点间的冲突和串扰,达到延长整体网络寿命的效果。     

On the Planning of Wireless Sensor Networks: Energy-Efficient Clustering under the Joint Routing and Coverage Constraint

Minimizing energy dissipation and maximizing network lifetime are important issues in the design of applications and protocols for sensor networks. Energy-efficient sensor state planning consists in finding an optimal assignment of states to sensors in order to maximize network lifetime. For example, in area surveillance applications, only an optimal subset of sensors that fully covers the monitored area can be switched on while the other sensors are turned off. In this paper, we address the optimal planning of sensors' states in cluster-based sensor networks. Typically, any sensor can be turned on, turned off, or promoted cluster head, and a different power consumption level is associated with each of these states. We seek an energy-optimal topology that maximizes network lifetime while ensuring simultaneously full area coverage and sensor connectivity to cluster heads, which are constrained to form a spanning tree used as a routing topology. First, we formulate this problem as an Integer Linear Programming model that we prove NP-Complete. Then, we implement a Tabu search heuristic to tackle the exponentially increasing computation time of the exact resolution. Experimental results show that the proposed heuristic provides near-optimal network lifetime values within low computation times, which is, in practice, suitable for large-sized sensor networks.     

On coverage issues in directional sensor networks: A survey

The coverage optimization problem has been examined thoroughly for omni-directional sensor networks in the past decades. However, the coverage problem in directional sensor networks (DSN) has newly taken attraction, especially with the increasing number of wireless multimedia sensor network (WMSN) applications. Directional sensor nodes equipped with ultrasound, infrared, and video sensors differ from traditional omni-directional sensor nodes with their unique characteristics, such as angle of view, working direction, and line of sight (LoS) properties. Therefore, DSN applications require specific solutions and techniques for coverage enhancement. In this survey article, we mainly aim at categorizing available coverage optimization solutions and survey their problem definitions, assumptions, contributions, complexities and performance results. We categorize available studies about coverage enhancement into four categories. Target-based coverage enhancement, area-based coverage enhancement, coverage enhancement with guaranteed connectivity, and network lifetime prolonging. We define sensing models, design issues and challenges for directional sensor networks and describe their (dis)similarities to omni-directional sensor networks. We also give some information on the physical capabilities of directional sensors available on the market. Moreover, we specify the (dis)advantages of motility and mobility in terms of the coverage and network lifetime of DSNs.     

A distributed activity scheduling algorithm for wireless sensor networks with partial coverage

One of the most important design objectives in wireless sensor networks (WSN) is minimizing the energy consumption since these networks are expected to operate in harsh conditions where the recharging of batteries is impractical, if not impossible. The sleep scheduling mechanism allows sensors to sleep intermittently in order to reduce energy consumption and extend network lifetime. In applications where 100% coverage of the network field is not crucial, allowing the coverage to drop below full coverage while keeping above a predetermined threshold, i.e., partial coverage, can further increase the network lifetime. In this paper, we develop the distributed adaptive sleep scheduling algorithm (DASSA) for WSNs with partial coverage. DASSA does not require location information of sensors while maintaining connectivity and satisfying a user defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information. The performance of DASSA is compared with an integer linear programming (ILP) based centralized sleep scheduling algorithm (CSSA), which is devised to find the maximum number of rounds the network can survive assuming that the location information of all sensors is available. DASSA is also compared with the decentralized DGT algorithm. DASSA attains network lifetimes up to 92% of the centralized solution and it achieves significantly longer lifetimes compared with the DGT algorithm.     

WHOMoVeS: An optimized broadband sensor network for military vehicle tracking

With the advance of sensing technologies and their applications, advanced sensor networks are gaining increasing interest. For certain sensitive applications, heterogeneous sensors can be deployed in the monitored space to ensure scalability, high-speed communication, and long network lifetime. Hybrid sensor networks have capabilities to combine the use of both resource-rich and resource-impoverished sensor nodes. This paper proposes a heterogeneous broadband sensor network architecture for military vehicle tracking. Powerful sensor devices with good bandwidth and energy capabilities are used as a communication backbone while energy sensors are used to track moving targets. Copyright © 2007 John Wiley & Sons, Ltd.     

A Coverage-Based Scheduling Algorithm for WSNs

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.     

SERP: secure energy-efficient routing protocol for densely deployed wireless sensor networks

In this paper, we present secure energy-efficient routing protocol (SERP) for densely deployed wireless sensor networks which aims to achieve robust security for transmitted sensor readings with an energy-efficient network backbone. When the sensors with limited energy budgets are deployed in hazardous environment, ensuring energy efficiency and security of the sensor readings becomes a crucial task. Here, we address how to deal with such a deployment scenario. Our protocol ensures secure transmission of data from the source sensors to the base station in a way that it can best utilize the available amount of energy in the network. We use one-way hash chain and pre-stored shared secret keys for ensuring data transmission security. In SERP, first, a sink rooted tree structure is created as the backbone of the network. This energy-efficient network structure is used for authenticated and encrypted data delivery from the source sensors to the base station. To introduce data freshness, SERP includes an optional key refreshment mechanism which could be applied depending on the application at hand. Our analysis and simulation results show that SERP provides a good level of confidentiality and authenticity of data that are transmitted from the sensors to the base station. It also helps for energy-efficient structuring of the network so that the maximum lifetime of the network could be achieved.     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

Energy-Aware Optimization Model in Chain-Based Routing

Sensor networks are deployed in numerous military and civil applications, such as remote target detection, weather monitoring, weather forecast, natural resource exploration and disaster management. Despite having many potential applications, wireless sensor networks still face a number of challenges due to their particular characteristics that other wireless networks, like cellular networks or mobile ad hoc networks do not have. The most difficult challenge of the design of wireless sensor networks is the limited energy resource of the battery of the sensors. This limited resource restricts the operational time that wireless sensor networks can function in their applications. Routing protocols play a major part in the energy efficiency of wireless sensor networks because data communication dissipates most of the energy resource of the networks. The above discussions imply a new family of protocols called chain-based protocols. In the protocols, all sensor nodes sense and gather data in an energy efficient manner by cooperating with their closest neighbors. The gathering process can be done until an elected node calculates the final data and sends the data to the base station. In our works, we have proposed two methods to optimize the lifetime of chain-based protocols using Integer Linear Programming (ILP) formulations. Also, a method to determine the bounds of the lifetime for any energy-efficient routing protocol is presented. Finally, simulation results verify the work in this chapter. Furthermore, previous researches assume that the base station position is randomly placed without optimization. In our works, a non convex optimization model has been developed for solving the base station location optimization problem.     

Traffic-Aware Density-Based Sleep Scheduling and Energy Modeling for Two Dimensional Gaussian Distributed Wireless Sensor Network

Sleep scheduling of sensors in network domain is considered to be the most fundamental way of achieving higher life expectancy of wireless sensor networks. In this paper we have proposed density-based sleep scheduling strategy with traffic awareness in Gaussian distributed sensor network for minimizing energy consumption. In uniform distributed sensor network, it has been found that nodes in the nearest belt around the sink consume more energy. The reason behind is that the nodes near the sink involve more packet relaying load than the distant nodes. Consequently, the energy of these sensors get exhausted rapidly, thereby creating connectivity breaks known as energy hole. For this purpose, Gaussian distribution is used by densely deploying nodes around the sink which well-balances the relaying load. In addition, we have developed the analytical model for computing the energy consumption and coverage analysis in the sensor network. The performance of our sleep scheduling method is evaluated with respect to the Randomized Scheduling and Linear Distance-based Scheduling protocols. The simulation results of our proposed work show commendable improvement in network lifetime.     

Analysis and modeling of task scheduling in wireless sensor network based on divisible load theory

In this paper, we propose a novel task scheduling algorithm (Divisible Task scheduling Algorithm for Wireless sensor networks (DTAW)) based on divisible load theory in heterogeneous wireless sensor networks to complete the tasks within the shortest possible time and reduce the sensors' energy‐consuming. In DTAW, the tasks are distributed to the wireless sensor network by the (SINK) on the basis of the processing and communication capacity of each sensor. After receiving the subtasks, the intracluster sensors carry out its tasks simultaneously and send the results to cluster head sequentially. By removing communication interference between each sensor, reduced task completion time and improved network resource utilization are achieved. Each cluster head simultaneously finishes sending fused data to the SINK after fusing the data obtained from intracluster sensors. In this way, the overlap between the task performing and communication phase would be much better. Simulation results are presented to demonstrate the impacts of different network parameters on the makespan and energy consumption. The results show that the algorithm enables to reasonably distribute tasks to each sensor and then effectively reduces the time‐consuming and energy‐consuming. Copyright © 2012 John Wiley & Sons, Ltd.     

Minimum latency joint scheduling and routing in wireless sensor networks

Wireless sensor networks are expected to be used in a wide range of applications from environment monitoring to event detection. The key challenge is to provide energy efficient communication; however, latency remains an important concern for many applications that require fast response. In this paper, we address the important problem of minimizing average communication latency for the active flows while providing energy-efficiency in wireless sensor networks. As the flows in some wireless sensor network can be long-lived and predictable, it is possible to design schedules for sensor nodes so that nodes can wake up only when it is necessary and asleep during other times. Clearly, the routing layer decision is closely coupled to the wakeup/sleep schedule of the sensor nodes. We formulate a joint scheduling and routing problem with the objective of finding the schedules and routes for current active flows with minimum average latency. By constructing a novel delay graph, the problem can be solved optimally by employing the M node-disjoint paths algorithm under FDMA channel model. We further present extensions of the algorithm to handle dynamic traffic changes and topology changes in wireless sensor networks.     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime.