Making Aggregation Scheduling Usable in Wireless Sensor Networks: An Opportunistic Approach

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.

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.

MC-MLAS: Multi-channel Minimum Latency Aggregation 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.

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).

This paper proposes a reliable data aggregation scheduling that uses caching and re-transmission based on track topology. In the proposed scheme, a node detects packet losses by overhearing messages that includes error indications of the child nodes, from its neighbor nodes. If packet losses are detected, as a backup parent, the node retransmits the lost packet. A retransmission strategy is added into the adaptive timeout scheduling scheme, which adaptively configures both the timeout and the collection period according to the potential level of an event occurrence. The retransmission steps cause an additional delay and power consumption of the sensor nodes, but dramatically increase the data accuracy of the aggregation results. An extensive simulation under various workloads shows that the proposed scheme outperforms other schemes in terms of data accuracy and energy consumption.

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 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.

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.

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.

Aggregation is an important and commonplace operation in wireless sensor networks. Due to wireless interferences, aggregation in wireless sensor networks often suffers from packet collisions. In order to solve the collisioa problem, aggregation scheduling is extensively researched in recent years. In many sensor network applications such as real-time monitoring, aggregation time is the most concerned performance. This paper considers the minimum-time aggregation scheduling problem in duty-cycled wireless sensor networks for the first time. We show that this problem is NP-hard and present an approximation algorithm based on connected dominating set. The theoretical analysis shows that the proposed algorithm is a nearly-constant approximation. Simulation shows that the scheduling algorithm has a good performance.

Wireless Sensor Network (WSN) is a very important research area. It has wide range of applications in different domains, such as industrial production processes and military fields. The sensing of events and forwarding of sensed information, through multi-hops wireless communications, toward the base-station is the main functionality of a WSN. In this paper, we study the problem of how gather and aggregate data from the whole sensor nodes to the base-station, with efficient way, such that the energy consumption is reduced, messages collision are prevented and the time latency is minimized. The problem of data aggregation scheduling is mathematically modeled, and then a heuristic to resolve the problem are proposed. The simulation results show the efficiency of our solution compared to the best approaches existed in literature.KeywordsSensorsWireless Sensor Networkdata aggregation schedulingHeuristicmathematical modeling

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

Thanks to the recent advances in energy-harvesting devices, nodes equipped with such devices are produced and enable Wireless Sensor Networks (WSNs) to be energy self-sustainable. Such networks are named as Battery-Free WSNs (BF-WSNs). Data aggregation is an essential operation in WSNs, and the Minimum Latency Aggregation Scheduling (MLAS) problem which seeks a collision-free aggregation scheduling with the minimum latency has been well studied in Battery-Powered WSNs (BP-WSN). In BP-WSNs, latency is mainly caused by the time overhead in collision-avoiding. However, the time-consumption for node recharging is the main cause of latency in BF-WSNs. Moreover, the collisions are time-independent while the recharge rate is time-varying. Therefore, the previous algorithms are not suitable for BF-WSNs. In addition, if aggregating data from all nodes, the latency would be determined by the node with the lowest recharge rate. Thus, we propose to aggregate a subset of nodes which can meet the given coverage quality requirement. Meanwhile, the aggregation tree and scheduling strategy should be adaptive to the current energy condition. We formulate this problem and propose a distributed algorithm which can select nodes adaptively according to their energy condition and schedule these nodes to achieve the minimum latency, simultaneously. To the best of our knowledge, it is the first distributed algorithm to solve the MLAS problem with coverage guarantee in BF-WSNs. The simulation results verify that our algorithm can reduce aggregation latency effectively, especially in bad energy condition.

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.