Latency In Wireless Sensor Networks Research Articles

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

Heuristic mobile data gathering for wireless sensor networks via trajectory control

This article focuses on the problem of scheduling the optimal paths of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize the travel distance and balance the energy consumption and the data gathering latency in wireless sensor networks for smart cities. To partition the network for the multiple mobile elements and compute the trajectories of the multiple mobile elements, we utilize the sensor’s communication range and construct a multiple mobile elements scheduling problem. A heuristic mobile data gathering approach is proposed to solve this problem, which includes the following three steps. The sensor nodes are preliminarily partitioned into four levels, and then the clusterheads are further partitioned, and the traveling tour is scheduled for each cluster. After the first two steps, all the sensor nodes are partitioned reasonably for the multiple mobile elements. In the last step, the traveling tour is scheduled for each cluster, and the meeting point of each clusterhead is determined. We compare the proposed heuristic mobile data gathering with the existing approaches. The results indicate that the travel distance and the data gathering latency are reduced significantly, which further validates that the communication range is beneficial to minimize the travel distance.

Wireless sensor network design for smart grids and Internet of things for ambient living using cross-layer techniques

Due to the increased demand of wireless sensor networks for their characteristics like low energy consumption, robustness, and low cost in several demanding and complex applications like smart grid, health and safety, traffic and weather updates, there is need of monitoring the infrastructure in a timely manner with high reliability. In this article, a cross-layer data communication scheme target-aware cross-layer technique is proposed to enhance reliability and to reduce the latency in wireless sensor networks. The proposed scheme uses connected dominating set at network layer where nodes in the connected dominating set are directly connected to all the other nodes in the network. Each sensor node sends the data to the nodes present in the connected dominating set, which forwards it to their respective destinations. The proposed scheme reduces the chances of collision resulting reduced delays. Higher packet delivery ratio is achieved with the proposed scheme results in improved reliability. The proposed scheme is outperforming other state-of-the-art schemes in terms of packet delivery ratio, latency, and data throughput with the default 802.15.4 and delay-responsive cross layer in both static and mobile scenarios using network simulator tool.

Delay Tolerance and Energy Saving in Wireless Sensor Networks with a Mobile Base Station

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile devices. A mobile device roams sensing fields and collects data from sensors through a short transmission range. Multihop communication is used to improve data gathering by reducing the tour length of the mobile device. In this paper we study the trade-off between energy saving and data gathering latency in wireless sensor networks. In particular, we examine the balance between the relay hop count and the tour length of a mobile Base Station (BS). We propose two heuristic algorithms, Adjacent Tree-Bounded Hop Algorithm (AT-BHA) and Farthest Node First-Bounded Hop Algorithm (FNF-BHA), to reduce energy consumption of sensor nodes. The proposed algorithms select groups of Collection Trees (CTs) and a subset of Collection Location (CL) sensor nodes to buffer and forward data to the mobile BS when it arrives. Each CL node receives sensing data from its CT nodes within bounded hop count. Extensive experiments by simulation are conducted to evaluate the performance of the proposed algorithms against another heuristic. We demonstrate that the proposed algorithms outperform the existing work with the mean of the length of mobile BS tour.

Adaptive Sleep Efficient Hybrid Medium Access Control algorithm for next-generation wireless sensor networks

The scheduling algorithm is a fundamental design problem to allocate resources amongst different entities in distributive wireless sensor networks (WSNs). These sensor nodes have limited power and non-replenishable energy resources. In WSNs, the duty cycling mechanism is commonly used to save energy due to idle listening. On the other hand, a fixed duty cycling mechanism increases transmission latency in WSNs. Therefore, in order to ensure the prolonged network-life of WSNs, the medium access control (MAC) protocol should be tackled in an efficient manner to improve energy efficiency by minimizing idle listening, maximizing sleep duration, and eliminating data collision. This paper proposes a practical Adaptive Sleep Efficient Hybrid Medium Access Control (AEH-MAC) algorithm in which the key idea is to dynamically adjust nodes’ sleep time to improve the scheduling in WSNs. The AEH-MAC allows nodes to adjust sleep time dynamically according to the traffic load and coordinate wakeup time with neighbour nodes. A series of short taken packets are transmitted to wake the receiver, and a prediction field is introduced in the ACK packets (GRANT/RELEASE) to reduce the waiting time of the source node. In the proposed algorithm, each node maps a conflict-free time slot for itself up to two-hop neighbouring nodes. The simulation results show that the AEH-MAC algorithm achieves high performance in terms of runtime, number of rounds, energy consumption, and slot reservation.

Fast convergecast for low-duty-cycled multi-channel wireless sensor networks

Convergecast is a fundamental operation in many wireless sensor network (WSN) applications. To conserve energy, many previous WSN protocols discuss to periodically schedule active timings (or say slots) of transmission links in the network. When collecting data, the slots should be carefully assigned to conserve latency. Recently, the multichannel concept is utilized to facilitate slot assignment. When the network has multiple channels, the convergecast latency can be further reduced since the interferences between transmission links can be eliminated. In this work, we model the above scenario as a minimal delay scheduling(MDS) problem, and prove it as an NP-complete problem. We propose a heuristic algorithm, which contains three phases. In our design, the first phase connects nodes by a shortest path tree with constrained degrees. Then, the second phase assigns slots to links to achieve optimal report latency (regardless of interferences). Finally, the third phase assigns frequency channels to nodes to eliminate interferences between links, and carefully adjust some slots if necessary. Simulation and implementation results indicate that the proposed scheme can effectively reduce the convergecast latency in WSNs with multiple channels.

DS-MAC: An energy efficient demand sleep MAC protocol with low latency for wireless sensor networks

Duty cycling mechanism has been widely used to conserve energy that consumed by idle listening in wireless sensor networks, while fixed duty cycling introduces transmission latency in packet delivery. End to end latency is one of the most significant factors of packets loss in wireless sensor nodes, and many techniques have been proposed based on listening adaptively to reduce delay, which are mainly designed for light traffic loads. In this paper, we propose a novel asynchronous duty cycling MAC protocol, called demand sleep MAC (DS-MAC) that allows nodes to adjust their sleep time adaptively according to the amount of the received data packets in order to efficient and effective communication in the dynamic traffic load. DS-MAC protocol attempts to transmit a series of short token packets to wake up the receiver, which avoids the overhearing problem. Nodes in DS-MAC put the prediction field into ACK packets, which decreases the waiting delay of source node. Comprehensive simulation shows that when there are variable flows, such as broadcast traffic or transmissions from hidden nodes, DS-MAC significantly decreases waiting delay and energy consumption.

Energy-Aware Link Scheduling Protocol for Wireless Sensor Networks

By reducing interferences drastically, time division multiple access (TDMA) based approaches are considered one of the most efficient solutions to optimize resources' use. The existing protocols, however, address only the latency minimization without considering the waste of energy, which typically results from idle listening or frequent transitions of the radio module between sleep and active modes. Besides, only saturated systems are considered in these protocols, which may imply resources' underutilization in some practical use cases. In this paper, we present an energy-aware TDMA-based link scheduling protocol, named deterministic link scheduling protocol (DLSP), designed with the aim of achieving both low energy consumption and low latency in wireless sensor networks. DLSP takes advantage of the spatial reuse of interference-free time slots using conflict graphs. Unlike earlier studies that often considered saturated traffic, we propose to relax the saturation assumption in order to maintain good performance when some of the nodes have no data to send. Thus, we propose to define the following transmission periods: a period to send the own data of the nodes and a period to relay packets. The simulation results clearly show the effectiveness of the proposed protocol, in terms of latency and energy consumption, compared to existing approaches.

Minimum Latency Multiple Data MULE Trajectory Planning in Wireless Sensor Networks

This paper investigates the problem of computing the optimal trajectories of multiple data MULEs (e.g., robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks. By relying on a slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood ( k-TSPN) and the k-rooted path cover problem with neighborhood ( k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average.

A Progressive Approach to Reducing Data Collection Latency in Wireless Sensor Networks with Mobile Elements

The introduction of mobile elements has created a new dimension to reduce and balance the energy consumption in wireless sensor networks. However, data collection latency may become higher due to the relatively slow travel speed of mobile elements. Thus, the scheduling of mobile elements, i.e., how they traverse through the sensing field and when they collect data from which sensor, is of ultimate importance and has attracted increasing attention from the research community. Formulated as the traveling salesman problem with neighborhoods (TSPN) and due to its NP-hardness, so far only approximation and heuristic algorithms have appeared in the literature, but the former only have theoretical value now due to their large approximation factors. In this paper, following a progressive optimization approach, we first propose a combine-skip-substitute (CSS) scheme, which is shown to be able to obtain solutions within a small range of the lower bound of the optimal solution. We then take the realistic multirate features of wireless communications into account, which have been ignored by most existing work, to further reduce the data collection latency with the multirate CSS (MR-CSS) scheme. Besides the correctness proof and performance analysis of the proposed schemes, we also show their efficiency and potentials for further extensions through extensive simulation.

On bounding node-to-sink latency in wireless sensor networks with multiple sinks

Bounding node–to–sink latency is an important issue of wireless sensor networks (WSNs) with a quality of service requirement. This paper proposes to deploy multiple sinks to control the worst case node–to–sink data latency in WSNs. The end–to–end latency in multihop wireless networks is known to be proportional to the hop length of the routing path that the message moves over. Therefore, we formulate the question of what is the minimum number of sinks and their locations to bound the latency as the minimum d–hop sink placement problem. We also consider its capacitated version. We show problems are NP–hard in unit disk graph (UDG) and unit ball graph, and propose constant factor approximations of the problems in both graph models. We further extend our algorithms so that they can work well in more realistic quasi UDG model. A simulation study is also conducted to see the average performance of our algorithms.

An Energy-Efficient Aggregation Scheduling Algorithm with Minimal Latency in Wireless Sensor Networks

In this paper, we address the problem of data aggregation scheduling problem to minimize the latency in wireless sensor networks (WSNs). An efficient distributed synchronous aggregation scheduling method is proposed to structure a collision-free schedule for data aggregation in WSNs. By using a connected dominating set (CDS) as an aggregation tree, we implement the synchronous aggregation scheduling. We prove that the latency of the aggregation schedule generated by our algorithm is at most 4 15 16 R    time-slots where R is the network radius and  is the maximum node degree in the communication graph of the original network. Analysis and simulation results show the validity and superiority of the algorithm.

On the data gathering capacity and latency in wireless sensor networks

In this paper, we investigate the fundamental properties of data gathering in wireless sensor networks, in terms of both capacity and latency. We consider a scenario in which s(n) out of n total network nodes have to deliver data to a set of d(n) sink nodes at a constant rate λ(n, s(n), d(n)). The goal is to characterize the maximum achievable rate, and the latency in data delivery. We present a simple data gathering scheme that achieves asymptotically optimal data gathering capacity and latency with arbitrary network deployments when d(n) = 1, and for most scaling regimes of s(n) and d(n) when d(n) > 1 in case of square grid and random node deployments. Differently from most previous work, our results and the presented data gathering scheme do not sacrifice energy efficiency to the need of maximizing capacity and minimizing latency. Finally, we consider the effects of a simple form of data aggregation on data gathering performance, and show that capacity can be increased by a factor f(n) with respect to the case of no data aggregation, where f(n) is the node density. To the best of our knowledge, the ones presented in this paper are the first results showing that asymptotically optimal data gathering capacity and latency can be achieved in arbitrary networks in an energy efficient way.

A New Scheduling Algorithm for Reducing Data Aggregation Latency in Wireless Sensor Networks

Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- gation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works.

An Energy Efficient Cluster Formation Protocol with Low Latency In Wireless Sensor Networks

Data gathering is an essential operation in wireless sensor network applications. So it requires energy efficiency techniques to increase the lifetime of the network. Similarly, clustering is also an effective technique to improve the energy efficiency and network lifetime of wireless sensor networks. In this paper, an energy efficient cluster formation protocol is proposed with the objective of achieving low energy dissipation and latency without sacrificing application specific quality. The objective is achieved by applying randomized, adaptive, self-configuring cluster formation and localized control for data transfers. It involves application specific data processing, such as data aggregation or compression. The cluster formation algorithm allows each node to make independent decisions, so as to generate good clusters as the end. Simulation results show that the proposed protocol utilizes minimum energy and latency for cluster formation, there by reducing the overhead of the protocol. Keywords—Sensor networks, Low latency, Energy sorting protocol, data processing, Cluster formation.

APC-MAC/TA: Adaptive Power Controlled MAC Protocol with Traffic Awareness for Wireless Sensor Networks

In this paper, we propose an adaptive power controlled MAC protocol with a traffic-aware scheme specifically designed to reduce both energy and latency in wireless sensor networks. Typically, existing MAC protocols for sensor networks sacrifice latency performance for node energy efficiency. However, some sensor applications for emergencies require rather fast transmissions of sensed data, where we need to consider both energy and latency together. The proposed MAC protocol includes two novel ideas: one is a transmission power control scheme for improving latency in high traffic loads, and the other is a traffic-aware scheme to save more energy in low traffic loads. The transmission power control scheme increases channel utilization by mitigating interference between nodes, and the traffic-aware scheme allows nodes to sleep to reduce idle energy consumption when there are no traffic loads in a network. Simulation results show that the proposed protocol significantly reduces the latency as well as the energy consumption compared to the S-MAC protocol specifically for a large transmission power of nodes and low network traffic.

Optimized asset planning for minimizing latency in wireless sensor networks

Wireless Sensor Networks (WSNs) has been attracting lots of interest in recent years. In such networks sensors data are collected over multi-hop routes at one or multiple base-stations (gateway nodes) for processing. In many WSN applications such as disaster management and combat field surveillance, rapid response to detected events is necessary and thus data latency should be minimal. Given the sensor's energy and radio range constraints, direct communication with the gateway is inefficient and often infeasible for most deployed sensors. An intuitive approach to limit data latency is to increase the population of gateways and place them in the vicinity of sensors. However, gateway nodes are typically costly and thus it is desired to limit their count. Therefore, there is a need to balance between such conflicting requirements. In this paper, we pursue an integrated approach to asset planning in WSNs so that the data latency is minimized. The goal is to determine the least number of gateways and identify where to place them in the network in order to achieve a certain delay bound on data delivery. We formulate an optimization model for the asset planning problem and present effective algorithms for solving it. The proposed solution scheme employs contemporary search heuristics such as k-means and genetic algorithms. Validation results confirm the effectiveness of our approach in achieving the desired design goals.