Cooperative mobile data collection in smart cities

With decrease in the cost and the size of sensor devices, we can envision a world of ubiquitous sensor networks. Usually, sensor data needs to be disseminated from the source to data collectors, making the spatially distributed sensor data available for applications. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDCs) to collect and deliver the sensor data. The goal of this work is to design a dissemination protocol that leads to efficient data delivery from the source sensors to ubiquitous MDCs. We propose the Wait-Focus-Spray (WFS) data delivery scheme for wireless sensor networks with ubiquitous MDCs. The main objective of WFS is to balance the data delivery latency and transmission overhead when considering the existence of ubiquitous MDCs. In WFS, we also propose a corresponding mechanism-probabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting a MDC. We then present an analytical model based on the Markov chain model to analyze the trade-off between delivery latency and transmission cost in WFS. Through extensive simulations, we demonstrate that our proposed scheme reduces the transmission cost per message while provides comparable delivery delay compared with the alternative approach.

In the era of New Generation Networks (NWGN) many emerging and future enable technologies accomplish the requirements of ubiquitous communication networks. Mobile wireless sensor network (mWSN) is one of the rising and emerging technologies for various application of NWGN, the enormous concerns of these networks are data gathering with energy efficiency. Routing protocol act as middleware, which is responsible to enhance the network performance with less energy consumption. In this paper we propose, analyze and validate mobile data collector (MDC) based LEACH routing protocol for environmental application which is based on multi-hop routing strategy. MDC based LEACH routing protocol utilizes self-organized sensor nodes with distributed cluster formation technique, randomly selection of cluster heads to equally balance the energy consumption among the sensor nodes and finally forward the data to base station by the support of MDC. Moreover, our approach shows the significant improvement in terms of energy consumption of sensor nodes, enhance network lifetime and data gathering at base station are outperform when compared with LEACH protocol. Key words: Mobile wireless sensor networks, wireless sensor networks, network protocol, cluster based routing, data gathering technique.

Secure time synchronization is a key requirement for many sophisticated application running on these networks. Most of the existing secure time synchronization protocols incur high communication and storage costs and are subject to a few known security attacks. In wireless sensor network (WSN), lifetime of the network is determined by the amount of energy consumption by the nodes. To improve the lifetime of the network, nodes are organized into clusters, in which the cluster head (CH) collects and aggregates the data. A special node called mobile data collector (MDC) is used to collect the data from the CH and transfer it to the base station (BS) By using proposed method MDC authenticated to CH by computing shared secret keys on the fly. Once the MDC and CH are authenticated, all the sensor nodes in the cluster are synchronized, time synchronization reduce the communication and storage requirements of each CH. Security analysis of this proposed system shows that it is highly robust against different attacks namely compromised CH, reply attack, message manipulation attack as well as pulse delay attack.

This paper presents a model for ensuring data integrity using anomalous node identification in non-homogeneous wireless sensor networks (WSNs). We propose the anomaly detection technique while collecting data using mobile data collectors (MDCs), which detect the malicious activities before sending to the base station (BS). Our technique also protects the leader nodes (LNs) from malicious activities to ensure data integrity between the MDC and the LNs. The proposed approach learns the data characteristics from each sensor node and passes it to the MDC, where detection engine identifies the victim node and eventually alarm the LNs in order to keep the normal behaviour in the network. Our empirical evidence shows the effectiveness our approach.

Wireless sensor networks (WSNs) deployed in inhospitable environments are at increased risk of failure due to malicious enemy action, environmental causes or component malfunction. Large scale failure can divide a network into disjoint segments and disrupt its operation. Reestablishing inter-segment connectivity is of paramount importance to ensure that the network functions effectively. Mobile nodes within surviving disjoint segments can be used to serve as mobile data collectors (MDCs) to tour individual segments and reconnect them by ferrying messages. However finding shortest tours for MDCs is a NP hard problem. In this paper we present a mobility based polynomial time approach for internetworking of disjoint segments (MINDS) by using k MDCs to establish tours between segments. MINDs aims to not only minimize total tour length but also balance the load equitably between the k MDCs. MINDS uses a divide and conquer approach to successively split the larger tour into two around a central node at every step, until we obtain k optimized tours. MINDS is validated through simulation and is shown outperform competing schemes in the literature.

To alleviate energy hole problem inherent in static Base station (BS) scheme and extend network work of battery-restricted wireless rechargable sensor networks, a joint mobile data col-lection and adaptive charging scheduling (MDC-ACS) scheme based on virtual grid is proposed in this paper, aiming to achieve whole network energy balance and high charging effi-ciency. In MDC-ACS scheme, network is firstly divided into several grids and several rendezvous are determined using geo-graphic information. Then, mobile data collector (MDC) moves in grids for data collection within the application delay and moving trajectory is guided by rendezvous. Due to the limited battery capacity of mobile wirelsee charging vehicle (WCV) and its energy consumption while cruising, an adaptive charging schedule scheme is proposed for maximizing recharging benefit. With extensive simulation, we demonstrate that MDC-ACS scheme can achieve better charging benefit and reduce average charging delay.

In this paper, we propose a new data gathering mechanism for large scale wireless sensor networks by introducing mobility into the network. A mobile data collector, or for convenience called M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station and gathers data while moving through the field. An M-collector starts the data gathering tour periodically from the static data sink, polls each sensor while traversing the transmission range of the sensor, then collects data directly from the sensor without relay (i.e., in a single hop), finally returns and uploads data to the data sink. Since data packets are gathered directly without relay and collision, the lifetime of sensors is expected to be prolonged, and sensors can be made very simple and inexpensive. We focus on the problem of minimizing the length of each data gathering tour and refer to this problem as the single-hop data gathering problem, or SHDGP for short. We first formalize the SHDGP problem into a mixed integer program and prove its NP-hardness. We then present a heuristic tour-planning algorithm for a single M-collector. For the applications with a strict distance/time constraint for each data gathering tour, we also utilize multiple M-collectors to traverse through several shorter sub-tours concurrently to satisfy the distance/time constraint. Our single-hop data gathering scheme will improve the scalability and solve intrinsic problems of large scale homogeneous networks, and can be used in both connected networks and disconnected networks. The simulation results demonstrate that the new data gathering algorithm can greatly reduce the moving distance of the collectors compared to the covering line approximation algorithm, and is close to the optimal algorithm in small networks. In addition, the proposed data gathering scheme can prolong the network lifetime significantly compared to a network which has only a static data collector, or a network in which the mobile collector can only move along straight lines.

Extending the network lifetime is a challenging problem in wireless sensor networks. Using additional mobile agents called data collectors in order to gather data from sensor nodes and carry them to the sink, reveals a convenient solution. Intuitively, a great sensor nodes energy conservation can be achieved by single hop data gathering strategies. However, these strategies may lead to significantly increase the data latency in the network. Previous studies propose to introduce local relay transmissions in order to reduce the data gathering time. Most of these strategies rely on a static parameter in order to choose the data collector trajectory. In this paper, we focus on a novel data gathering strategy that aims at dynamically selecting the data collector trajectory according to the required data delivery delay deadline. In other words, the data collector may change its trajectory according to the urgency of the gathered data. For this purpose, we propose an algorithm for coloring the network area into red and green disks, according to the delay deadline required by the sensor nodes. Only the red disks must be visited during a tour of the data collector. Simulation results demonstrate that the proposed scheme significantly outperforms static cluster based gathering strategies by efficiently adapting the tolerated data latency to the consumed energy rate.

This paper studies decentralized strategies for facilitating data collection in circular wireless sensor networks, which rely on the stochastic diversity of data storage. The goal is to allow for a reduced delay collection by a mobile data collector (MDC) who accesses the network at a random position and random time. We consider a two-phase data collection: the push phase is mechanized through source packet dissemination strategies based on network coding; the pull phase is based on polling of additional packets from their original source nodes. Dissemination is performed by a set of relays which form a circular route to exchange source packets. The storage nodes within the transmission range of the routeiquests relays linearly combine and store overheard relay transmissions. MDC first collects a set of packets in its physical proximity and, using a message-passing decoder, attempts recovering all original source packets from this set. Whenever the decoder stalls a source packet which restarts decoding is polled/doped from its original source. The number of doped packets can be surprisingly small and, hence, the push-pull doping strategy may have the least collection delay when the density of source nodes is sufficiently large. Furthermore, the Ideal Soliton fountain encoding is a good linear combining strategy at the storage nodes whenever doping is employed.

In wireless sensor networks (WSN), generally sensors closest to the sink or base station tend to deplete their battery energy faster than other sensors and create an energy hole around the sink. In order to overcome this problem, a mobile data gathering using PSO and minimum covering spanning tree (MDG-PSO-MCST) is proposed. In this technique, multiple sensors are arranged to form clusters. Two SenCars with multiple antennas are deployed in the cluster location and space division multiple access (SDMA) technique is applied for data gathering with energy efficiency. The anchor points are selected for intermediate data collection based on the connectivity and node degree, node compatibility and the distance between the sensors of two adjacent clusters parameters using particle swarm optimisation (PSO) technique. The visiting tour of each Sencars among the selected anchors is performed using the MCST algorithm. By simulation results, we show that the proposed technique minimises the energy consumption and delay, and enhances the network efficiency.

Protect your data and I’ll rank its utility: A framework for utility analysis of anonymized mobility data for smart city applications

Mobile Data Collector device (MDC) is adopted to reduce the energy consumption in Wireless Sensor Networks. This device travels the network in order to gather the collected data from sensor nodes. This paper presents a new Tree Clustering algorithm with Mobile Data Collector in Wireless Sensor Networks, which establishes the shortest travelling path passing throw a subset of Cluster Heads (CH). To select CHs, we adopt a competitive scheme, and the best sensor nodes are elected according to the number of packets forwarded between sensor nodes, the number of hops to the tree’s root, the residual energy, and the distance between the node and the closest CH. In simulation results, we adopt the balanced and unbalanced topologies and prove the efficiently of our proposed algorithm considering the network lifetime, the fairness index and the energy consumption in comparison with the existing mobile data collection algorithms.

Energy-efficient data collection in strip-based wireless sensor networks with optimal speed mobile data collectors

Over the last decade, increasing availability of Internet of Things (IoT) devices and broadband communications technologies has enabled a number of smart applications in many cities. Among various smart city applications, a smart street lighting application is considered as one of the most cost-effective choices for a smart city today, if relying on existing street lights that are already deployed and networked across a city. Piggy-bagging on a readily available networked street lighting infrastructure, several city-wide services can be offered rapidly and cost effectively. Examples of additional devices that can be deployed along with networked street lights are such as environmental sensors, traffic sensors, IP cameras, digital signage, and more. To enable connections among different sensing and control devices, communication technologies and networks are of crucial components. There are a wide variety of communication technologies that can be used, namely, Sigfox, long range (LoRa), Weightless, Symphony Link, random phase multiple access (RPMA), narrowband IoT (NB-IoT), long-term evolution category 0/1/M (LTE category 0/1/M), power line communications (PLC), ZigBee, IPv6 low-power wireless personal area network (6LoWPAN), wireless local area network (WLAN), wireless mesh, worldwide interoperability for microwave access (WiMAX), and Cellular (2G/3G/ 4G/5G). The chapter aims at providing a comprehensive discussion of various communication technologies that can serve smart street lighting and other add-on applications in a smart city. These technologies are evaluated on the basis of communication network requirements of different applications pertaining to their data rate, latency, reliability, and security.

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data.