Data-Coverage Sleep Scheduling in Wireless Sensor Networks

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.

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.

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.

In Wireless Sensor Networks (WSNs), providing full area coverage while maintaining connectivity between the sensors is considered an important issue. Coverage-aware sleep scheduling is an efficient way to optimize the coverage of WSNs while maximizing the energy consumption. On the other hand, clustering can provide an efficient way to achieve high connectivity in WSNs. Despite the close relationship between the coverage problem and the clustering problem, they have been formulated, discussed and evaluated separately. Furthermore, most existing WSN strategies are designed to be applied on Two-Dimensional (2D) fields under an ideal energy consumption model that relies on calculating the Euclidean distance between any pair of sensors. In reality, sensors are mostly deployed in a Three-Dimensional (3D) field in many applications and they do exhibit a discrete energy consumption model that depends on the sensors' status rather than the distance between them. In this paper, we propose a Pareto-based network configuration strategy for 3D WSN s. In the proposed protocol, deciding the status of each sensor in a 3D WSN s is formulated as a single multi-objective minimization problem. The proposed formulation considers the following combined properties: energy efficiency, data delivery reliability, scalability, and full area coverage. The performance of the proposed protocol is tested in 3D WSNs and under a realistic energy consumption model which is based on the characteristics of the Chip con CC2420 radio transceiver data sheet.

In this paper, we propose a cross‐layer optimized geographic node‐disjoint multipath routing algorithm, that is, two‐phase geographic greedy forwarding plus. To optimize the system as a whole, our algorithm is designed on the basis of multiple layers' interactions, taking into account the following. First is the physical layer, where sensor nodes are developed to scavenge the energy from environment, that is, node rechargeable operation (a kind of idle charging process to nodes). Each node can adjust its transmission power depending on its current energy level (the main object for nodes with energy harvesting is to avoid the routing hole when implementing the routing algorithm). Second is the sleep scheduling layer, where an energy‐balanced sleep scheduling scheme, that is, duty cycle (a kind of node sleep schedule that aims at putting the idle listening nodes in the network into sleep state such that the nodes will be awake only when they are needed), and energy‐consumption‐based connected k‐neighborhood is applied to allow sensor nodes to have enough time to recharge energy, which takes nodes' current energy level as the parameter to dynamically schedule nodes to be active or asleep. Third is the routing layer, in which a forwarding node chooses the next‐hop node based on 2‐hop neighbor information rather than 1‐hop. Performance of two‐phase geographic greedy forwarding plus algorithm is evaluated under three different forwarding policies, to meet different application requirements. Our extensive simulations show that by cross‐layer optimization, more shorter paths are found, resulting in shorter average path length, yet without causing much energy consumption. On top of these, a considerable increase of the network sleep rate is achieved. Copyright © 2014 John Wiley & Sons, Ltd.

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.

Mobile TV services are expected to become a key application in future wireless networks. The recently proposed terrestrial digital broadcast technology, DVB-H, especially designed for mobile services, is regarded as a powerful alternative to the 3G cellular networks that already offer these services out on a point-to-point basis. Unfortunately, the cost of supporting real-time streaming and full area coverage seems to be very high compared to fixed TV services. In this article we discuss hybrid IP datacast DVB-H and 3G systems as a possible solution for providing affordable network infrastructure and services. Our approach is DVB-H centric. The 3G cellular system plays the role of providing interactivity, error reporting, and repair for the DVB-H broadcast channel. To enable an easy and efficient interworking of DVB-H and 3G, we employ forward error correction at the application layer with digital fountain coding. The main way to provide lower cost services is to avoid full DVB-H area coverage from the beginning and to perform an incremental DVB-H network deployment that follows the user demand. In this direction, to hide the discontinuity in coverage from the perception of users, we propose to take advantage of the bursty character of DVB-H transmissions and the mobility of users. This is possible by sending additional parity data, either with the DVB-H or the cellular network, in the time intervals between original service bursts. We evaluate the potential DVB-H infrastructure cost savings determined by transmitting additional parity data in DVB-H when targeting vehicular users. The implications of delivering parity data through the cellular network also are discussed. The numerical investigations show a potential for significant cost savings compared with the traditional approaches.

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.

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.

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.

Cover scheduling for wireless sensor networks has attracted a lot of attention during the last decade. Many heuristic approaches and exact methods have been proposed for producing covers, i.e. subsets of sensors to be used at the same time. However, the actual schedule of the generated covers has never been explicitly addressed to the best of our knowledge. Though, this problem is of particular relevance when coverage breach is allowed in the aforementioned problems, i.e., when a full coverage of targets at any time is not mandatory. In that case, the objective of the wireless sensor network cover scheduling problem (WSN-CSP) is to schedule the covers so as to minimize the longest period of time during which a target is not covered in the schedule. In this paper, this problem is proved $\mathcal NP$ -Hard, a MILP formulation is provided along with a greedy heuristic and a genetic algorithm based approach. Computational results show the effectiveness of the last approach.

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).

One of the main objectives of query processing in wireless sensor networks is to achieve high resource efficiency. In this paper, we focus on scheduling multiple queries which may have temporal and spatial overlapping in querying operations. Our purpose is to schedule the operations on the sensor nodes so as to save the total cost by maximizing the overlapping in time among the queries. We propose an algorithm, called RSQS, which allows the newly arrived query to best share the operations with existing queries. The simulation results showed that our proposed approach can be used to effectively reduce the amount of sensory data transmitted in the network. This leads to energy and bandwidth saving in wireless sensor network.

Wireless Sensor Networks (WSNs) have a plethora of modern applications in our world and are also very promising and suitable for future applications. Critical issues such as clustering, organization, event scheduling (for transmitting and receiving), power consumption, routing, time response, etc., have been studied and analyzed in the literature. It is common knowledge that the most critical issue is the adaptation of the WSN in the real needs and dynamically changing environment. Despite the existing studies, the above issue has not been studied in depth. In a real WSN application the mote role, event scheduling, network clustering, and decision support are changing in a dynamically adapted manner. To this end, in this paper, a novel modeling approach based on the multi-agent technology is presented. Moreover, the proposed approach is focused on scheduling policy for achieving low power consumption.

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.