Energy-efficient coverage protocol based on stable and predictive scheduling in wireless sensor networks

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.

Scheduling sensor activities is an effective way to prolong the lifetime of wireless sensor networks (WSNs). In this paper, we explore the problem of wake-up scheduling in WSNs where sensors have different lifetime. A novel local wake-up scheduling (LWS) strategy is proposed to prolong the network lifetime with full coverage constraint. In the LWS strategy, sensors are divided into a first layer set and a successor set. The first layer set which satisfies the coverage constraint is activated at the beginning. Once an active sensor runs out of energy, some sensors in the successor set will be activated to satisfy the coverage constraint. Based on the LWS strategy, this paper presents an ant colony optimization based method, namely mc-ACO, to maximize the network lifetime. The mc-ACO is validated by performing simulations on WSNs with different characteristics. A recently published genetic algorithm based wake-up scheduling method and a greedy based method are used for comparison. Simulation results reveal that mc-ACO yields better performance than the two algorithms.

Wireless Sensor Networks (WSNs) consist of a large number of small and low cost sensor nodes powered by small batteries and equipped with various sensing devices. Usually, for many applications, once a WSN is deployed, probably in an inhospitable terrain, it is expected to gather the required data for quite some time, say for years. Since each sensor node has limited energy, these nodes are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. There are two major approaches to sleep scheduling of sensor nodes, viz. (i) random (ii) synchronized. Any sleep scheduling scheme has to ensure that data can always be routed from source to sink. In this paper, we propose a novel approach for sleep scheduling of sensor nodes using a tree and an energy aware routing protocol which is integrated with the proposed sleep scheduling scheme. The tree is rooted at the sink node. The internal nodes of the tree remain awake and the leaf nodes are made to sleep. This provides an assured path from any node to the sink node. The tree is periodically reconstructed considering the remaining energy of each node with a view to balance energy consumption of nodes, and remove any failed nodes from the tree. The proposed approach also considerably reduces average energy consumption rate of each node as we are able to put more number of nodes to sleep in comparison to other approaches. Additional fault-tolerance is provided by keeping two paths from each node towards the sink. Extensive simulation studies of the proposed routing protocol has been carried out using Castalia simulator, and its performance has been compared with that of a routing protocol, called GSP, which incorporates sleep scheduling using random approach. The simulation results show that the proposed approach has longer network lifetime in comparison to that provided by GSP, and the energy consumption of nodes is also balanced.

We consider the problem of scheduling in wireless sensor networks for the purposes of aggregation convergecast. We observe that existing schemes adopt essentially a two phase approach, consisting of, first, a tree construction and, second, a scheduling phase. Following a similar approach, we propose two new improvements, one to each of the two phases. Starting with a new lower bound on the schedule length, we make use of it in the tree construction phase. The tree construction phase consists of solutions to instances of bipartite graph semi-matchings. The scheduling phase is a weight-based priority scheme that obeys dependency (tree) and interference constraints. Our extensive experiments show that, overall, our proposed solution not only outperforms all previously proposed solutions in terms of schedule length, but it also significantly extends the network's lifetime.

The basic challenge in spatially distributed resource restricted Wireless Senor Network (WSN) is to prolong the network lifetime maintaining its performance criterions like energy efficiency, coverage and connectivity. To achieve this, scheduling can be performed in sensor nodes in terms of their wake up time periods. There are many traditional scheduling mechanisms developed. But, sensor node scheduling still poses a challenge when the WSN becomes dynamic in nature. In this paper, Deep Q-network modeling is carried out with Reinforcement scheduler. The advancement in reinforcement learning and particularly Deep Q-Network (DQN) algorithms provides an efficient tool for implementing intelligent scheduling. When an agent is trained using these techniques, its ability to differentiate between events improves thus, optimizing the scheduling process. The presented scheduling algorithm is evaluated and compared with traditional scheduling algorithms for the same network characteristics. The DQN modeling shows promising potential to intelligently schedule the sensor nodes in terms of energy consumption, average rewards and also minimum and maximum latencies.

Wireless Sensor Networks is dispersed by a group composed of sensors and Base Stations. It can be used in various applications of remote monitoring system. Sensor nodes route the collected data to the Base Station via multi-hop wireless links. These sensors typically use batteries to operate, the battery cannot be replaced when the depletion of energy resources. Therefore, how to use energy-saving sensor networks to extend the overall working time efficiently is an important subject. Our research is based on A Power Control Mechanism based on Sleep Scheduling for Wireless Sensor Networks. We proposed a refined mechanism of routing protocol for wireless sensor networks with sleep scheduling. In wireless sensor network with sleep scheduling, active node adjusts its working time and each step in sleeping state earlier. In the multi-hop transmission mode, each node broadcasts the packet to confirm the receiving node. Therefore, we use routing information to reduce the number of broadcasting during transmission. When route node with low rest power cannot transfer, in order to remain completed route, we can execute interrupt handler procedure to reduce the package loss. Finally, to achieve power efficiency, we will simulate if the proposed algorithm can reduce energy consumption effectively in the data transmission procedure.

Nodes in the wireless sensor networks are battery operated, and thus, the efficient use of the node's energy during wireless communication is pivotal for the long battery life. This paper addresses the challenges in energy-efficient data aggregation and transmission scheduling for each node by using time division multiple access (TDMA). We introduce multi-channel TDMA scheduling algorithms with the objective of minimizing the total energy consumption in the network. The proposed algorithms utilize multiple radio channels to bestow efficient scheduling while eliminating collisions and overhearing. First, we formulate an integer linear programming (ILP) algorithm that finds the minimum bound for the network's energy consumption. Then, we propose a near-optimal heuristic algorithm based on backtracking, which uses memoization to spurn the suboptimal schedules. Subsequently, we propose a computationally efficient heuristic algorithm by using Langford subset generation. The algorithm reduces the energy consumption in each timeslot while avoiding revisiting the same timeslot. We conducted extensive simulations to evaluate three proposed algorithms. The simulation results demonstrate that the proposed heuristic algorithms provide performance comparable to the optimum results of the ILP algorithm and reduce the magnitude of computation time.

Traditionally, Wireless Sensor Networks (WSNs) are deployed with a single sink. Due to the emergence of sophisticated applications, WSNs may require more than one sink, where many nodes forward data to many sinks. Moreover, deploying more than one sink may prolong the network lifetime and address fault tolerance issues. Several protocols have been proposed for WSNs with multiple sinks. However, they are either routing protocols or forward data from many nodes to one sink. In this paper, we propose data aggregation scheduling and energy-balancing algorithms for WSNs with multiple sinks that forward data from many nodes to many sinks. The algorithm first forms trees rooted at virtual sinks and then balances the number of children among nodes to balance energy consumption. Further, the algorithm assigns contiguous slots to sibling nodes to avoid unnecessary energy waste due to active-sleep transitions. We prove a number of theoretical results and the correctness of the algorithms. Simulation and testbed results show the correctness and performance of our algorithms.

This paper focuses on the distributed data aggregation collision-free scheduling problem, which is one of very important issues in wireless sensor networks. Bo et al. (Proc. IEEE INFOCOM, 2009) proposed an approximate distributed algorithm for the problem and Xu et al. (Proc. ACM FOWANC, 2009) proposed a centralized algorithm and its distributed implementation to generate a collision-free scheduling for the problem, which are the only two existing distributed algorithms. Unfortunately, there are a few mistakes in their performance analysis in Bo et al. (Proc. IEEE INFOCOM, 2009), and the distributed algorithm can not get the same latency as the centralized algorithm because the distributed implementation was not an accurate implementation of the centralized algorithm (Xu et al. in Proc. ACM FOWANC, 2009). According to those, we propose an improved distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks. Not an arbitrary tree in Bo et al. (Proc. IEEE INFOCOM, 2009) but a breadth first search tree (BFS) rooted at the sink node is adopted, the bounded latency 61R+5Δ?67 of the schedule is obtained, where R is the radius of the network with respect to the sink node and Δ is the maximum node degree. We also correct the latency bound of the schedule in Bo et al. (Proc. IEEE INFOCOM, 2009) as 61D+5Δ?67, where D is a diameter of the network and prove that our algorithm is more efficient than the algorithm (Bo et al. in Proc. IEEE INFOCOM, 2009). We also give a latency bound for the distributed implementation in Xu et al. (Proc. ACM FOWANC, 2009).

In this paper, a novel variable rate based time frame scheduling scheme is proposed to further reduce collisions and improve energy saving in wireless sensor networks. The MAC combines CSMA and TDMA functionalities, while obviating their shortcomings. After being assigned, slot 0 is preserved as the pre-schedule slot to inform neighbor nodes the schedule of the senders. During this pre-schedule slot, each node knows exactly the schedule of other neighbor nodes. Multi-rate and power scaling are adopted to achieve further energy saving by employing an acceptable rate rather than the maximum rate. Data rate is dynamically adjusted according to the traffic load of sending nodes in an energy efficient data rate to save energy. Compared with Z-MAC, performance in the present study proves to have better ability of local framing pre-schedule and multi-rate achieves better energy efficiency.

Review of nature-inspired methods for wake-up scheduling in wireless sensor networks

Sleep scheduling mechanisms have been widely used in wireless sensor networks so as to extend the lifetime of networks. Sensors are able to decide to be either in active or sleep mode to save the energy. Sensing coverage area is an important factor for some applications such as Intrusion Detection. It is necessary to have the full-sensing-covering set of active nodes on these applications. In this paper, we propose the Coverage Maximization with Sleep Scheduling protocol (CMSS) which is a decentralized protocol and maximize sensing coverage of the network. In our proposed solution, the area of network is divided into grid cells. Each sensor creates a neighbor table and transforms into cell-value table. These tables are used to make decision which mode it should be on each sensor. Simulation results show that CMSS not only consumes less overhead energy than MSCR, but also has a lower number of selected active nodes. Besides, communication range of sensors does not affect to the efficiency of networks like LDCC which exploits hop count information.

The problem of aggregation scheduling in wireless sensor networks is widely studied in order to minimize aggregation latency and prevent collisions. Many aggregation scheduling algorithms have been designed for time-critical applications. However, all of the existing work on aggregation scheduling suffers from packet losses due to the lossy links in wireless sensor networks. We observe that high data loss rates of over 90% often occur using existing scheduling algorithms when the link qualities are poor. This makes the aggregation scheduling algorithms impractical in real applications. This paper makes aggregation scheduling usable by proposing an opportunistic approach to improve the delivery rate by elaborately selecting opportunistic links for sensor nodes. Simulation results verify the efficiency of our approach. One of our methods can achieve delivery rates of close to 100% in various network conditions.

One key issue in networked systems that influences whether the deployed system will be able to provide timing guarantees is the Medium Access Control (MAC) protocol and its configurations. MAC protocols can be classified broadly into two categories: contention- and schedule-based. The contention-based protocols can easily adjust to the topology changes as new nodes may join and others may die after deployment. These protocols are based on Carrier Sense Multiple Access (CSMA) mechanisms and have higher costs due to potential interference, message collisions, overhearing, and idle listening than the schedule-based counterparts. Schedule-based protocols can avoid those problems by defining precise schedules, but they have strict time synchronization requirements. In this chapter we discuss scheduling approaches and mechanisms in wireless sensor networks. It sheds light on the medium access layer protocols, explains the mechanism of time-division medium access protocols, and finally how schedules are achieved within those protocols. The capacity to define schedules in wireless sensor networks is very important for planning operation-timings in the whole DCS.KeywordsWireless Sensor NetworksProvide Timing GuaranteesIdle ListeningTDMA ProtocolTime Division Multiple Access (TDMA)These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Minimum-Latency Aggregation Scheduling (MLAS) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for global-scale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log3n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [9]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.