A Search Strategy of Level-Based Flooding for the Internet of Things

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales

Strengths and Weaknesses of Prominent Data Dissemination Techniques in Wireless Sensor Networks

Data dissemination is the most significant task in a Wireless Sensor Network (WSN). From the bootstrapping stage to the full functioning stage, a WSN must disseminate data in various patterns like from the sink to node, from node to sink, from node to node, or the like. This is what a WSN is deployed for. Hence, this issue comes with various data routing models and often there are different types of network settings that influence the way of data collection and/or distribution. Considering the importance of this issue, in this paper, we present a survey on various prominent data dissemination techniques in such network. Our classification of the existing works is based on two main parameters: the number of sink (single or multiple) and the nature of its movement (static or mobile). Under these categories, we have analyzed various previous works for their relative strengths and weaknesses. A comparison is also made based on the operational methods of various data dissemination schemes

Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance

Efficient collection of sensor data via a new accelerated random walk

Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on random geometric graphs (RGG). Random walks in wireless sensor networks can serve as fully local, lightweight strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. In most cases, random walks are studied on graphs like Gn,p and grid. Instead, we here choose the RGG model, which abstracts more accurately spatial proximity in a wireless sensor network. We first evaluate an adaptive walk (the random walk with inertia) on the RGG model; its performance proved to be poor and led us to define and experimentally evaluate a novel random walk that we call γ-stretched random walk. Its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap and accelerate the cover time. We also define a new performance metric called proximity cover time that, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks

Analysis of Structured and Un-Structured Network Protocols for Data Aggregation Over Distributed Wireless Sensor Networks

The focus of this thesis is on design and evaluation of one-shot data aggregation protocols for static and mobile wireless sensor networks (WSNs). The goal in one-shot data aggregation is to compute a statistical summary of sensor data such as max, average, sum, count and min, when initiated by a special node such as the base station. WSNs have wide range of applications in both static and mobile/dynamic systems. Static sensor networks are especially useful when monitoring is required in harsh, inaccessible environments and when the region to be monitored is really large. Examples of static sensor network applications include environmental monitoring systems, monitoring of industrial control systems, monitoring of degradation in slagging gasifiers, distributed object detection and tracking. Example of mobile applications include vehicular ad-hoc networks and networks of personal radios used in emergency dispatch and battlefields.;For data aggregation in static networks with stable links, structured approaches such as spanning trees are generally preferred. This is because, once a data aggregation structure has been established, link topologies remain fixed and there is minimal need to actively maintain and change the routing structures. In this thesis, one such tree based data aggregation protocol has been designed and evaluated using simulations in networks ranging from 100-1000 nodes. The protocol has also been implemented at a smaller scale in the context of a smart refractory environment, where slag penetration in gasifiers is remotely monitored using smart bricks that are embedded with sensors. In mobile networks and networks with frequent link changes, topology driven structures are likely to be unstable and to incur a high communication overhead. Therefore, self-repelling random walks have been recently proposed as an attractive alternative for data aggregation in mobile systems. In this thesis, a brief overview of random walk based data aggregation has been presented and systematic evaluation of tree based and random walk based data aggregation protocols in networks ranging from 100-1000 nodes under varying degrees of node mobility has been done. The conditions under which unstructured protocols become more attractive in terms of convergence time and messaging efficiency as compared to tree based structured approaches have been quantified

Spiral Walk on Triangular Meshes : Adaptive Replication in Data P2P Networks

We introduce a decentralized replication strategy for peer-to-peer file exchange based on exhaustive exploration of the neighborhood of any node in the network. The replication scheme lets the replicas evenly populate the network mesh, while regulating the total number of replicas at the same time. This is achieved by self adaptation to entering or leaving of nodes. Exhaustive exploration is achieved by a spiral walk algorithm that generates a number of messages linearly proportional to the number of visited nodes. It requires a dedicated topology (a triangular mesh on a closed surface). We introduce protocols for node connection and departure that maintain the triangular mesh at low computational and bandwidth cost. Search efficiency is increased using a mechanism based on dynamically allocated super peers. We conclude with a discussion on experimental validation results

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Source location privacy in wireless sensor networks under practical scenarios : routing protocols, parameterisations and trade-offs

As wireless sensor networks (WSNs) have been applied across a spectrum of application domains, source location privacy (SLP) has emerged as a significant issue, particularly in security-critical situations. In seminal work on SLP, several protocols were proposed as viable approaches to address the issue of SLP. However, most state-of-the-art approaches work under specific network assumptions. For example, phantom routing, one of the most popular routing protocols for SLP, assumes a single source. On the other hand, in practical scenarios for SLP, this assumption is not realistic, as there will be multiple data sources. Other issues of practical interest include network configurations. Thus, thesis addresses the impact of these practical considerations on SLP. The first step is the evaluation of phantom routing under various configurations, e.g., multiple sources and network configurations. The results show that phantom routing does not scale to handle multiple sources while providing high SLP at the expense of low messages yield. Thus, an important issue arises as a result of this observation that the need for a routing protocol that can handle multiple sources. As such, a novel parametric routing protocol is proposed, called phantom walkabouts, for SLP for multi-source WSNs. A large-scale experiments are conducted to evaluate the efficiency of phantom walkabouts. The main observation is that phantom walkabouts can provide high level of SLP at the expense of energy and/or data yield. To deal with these trade-offs, a framework that allows reasoning about trade-offs needs to develop. Thus, a decision theoretic methodology is proposed that allows reasoning about these trade-offs. The results showcase the viability of this methodology via several case studies

Security of Software-defined Wireless Sensor Networks

Wireless Sensor Network (WSN) using Software Defined Networking (SDN) can achieve several advantages such as flexible and centralized network management and efficient routing. This is because SDN is a logically centralized architecture that separates the control plane from the data plane. SDN can provide security solutions, such as routing isolation, while handling the heterogeneity, scalability, and the limited resources of WSNs. However, such centralized architecture brings new challenges due to the single attack point and having non-dedicated channels for the control plane in WSNs. In this thesis, we investigate and propose security solutions for software-defined WSNs considering energy-efficiency and resource-preservation. The details are as follows. First, the functionality of software-defined WSNs can be affected by malicious sensor nodes that perform arbitrary actions such as message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in software-defined WSNs. Therefore, we design a hierarchical trust management scheme for software-defined WSNs (namely TSW) to detect potential threats inside software-defined WSNs while promoting node cooperation and supporting decision-making in the forwarding process. The TSW scheme evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the software-defined WSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Additionally, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is also defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communication and trust management threats such as black-hole, selective forwarding, denial of service, bad and good mouthing, and ON-OFF attacks. Second, network topology obfuscation is generally considered a proactive mechanism for mitigating traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined WSNs are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. As a result, we propose a new energy-aware network topology obfuscation mechanism, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: route overlapping, energy consumption, link costs, and node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the k-anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods strongly mitigate traffic analysis attacks and achieve effective network topology obfuscation for software-defined WSNs. In addition, the proposed methods reduce the success rate of the attacks while achieving lower energy consumption and longer network lifetime. Last, security networking functions, such as trust management and Intrusion Detection System (IDS), are deployed in WSNs to protect the network from multiple attacks. However, there are many resource and security challenges in deploying these functions. First, they consume tremendous nodes’ energy and computational resources, which are limited in WSNs. Another challenge is preserving the security at a sufficient level in terms of reliability and coverage. Watchdog nodes, as one of the main components in trust management, overhear and monitor other nodes in the network. Accordingly, a secure and energy-aware watchdog placement optimization solution is studied for software-defined WSNs. The solution balances the required energy consumption, computational resource, and security in terms of the honesty of the watchdog nodes. To this end, a multi-population genetic algorithm is proposed for the optimal placement of the watchdog function in the network given the comprehensive aspects of resources and security. Finally, simulation results demonstrate that the proposed solution robustly preserves security levels and achieves energy-efficient deployment. In summary, reactive and proactive security solutions are investigated, designed, and evaluated for software-defined WSNs. The novelty of these proposed solutions is not only efficient and robust security but also their energy awareness, which allows them to be practical on resource-constrained networks. Thus, this thesis is considered a significant advancement toward more trustworthy and dependable software-defined WSNs