Jiong Jin

Network architecture and QoS issues in the internet of things for a smart city

The emerging Internet of Things (IoT) that effectively integrates cyber-physical space to create smart environments will undoubtedly have a plethora of applications in the near future. Meanwhile, it is also the key technological enabler to create smart cities, which will provide great benefits to our society. In this paper, four different IoT network architectures spanning various smart city applications are presented and their corresponding network Quality of Service (QoS) requirements are defined. Furthermore, as the beneficiary of smart city, we have the responsibility to actively participate in its development as well. A new network paradigm, participatory sensing, is thus discussed as a special case to highlight the way people may be involved in the information acquisition-transmission-interpretation-action loop.

An Intelligent Task Allocation Scheme for Multihop Wireless Networks

Emerging applications in Multihop Wireless Networks (MHWNs) require considerable processing power which often may be beyond the capability of individual nodes. Parallel processing provides a promising solution, which partitions a program into multiple small tasks and executes each task concurrently on independent nodes. However, multihop wireless communication is inevitable in such networks and it could have an adverse effect on distributed processing. In this paper, an adaptive intelligent task mapping together with a scheduling scheme based on a genetic algorithm is proposed to provide real-time guarantees. This solution enables efficient parallel processing in a way that only possible node collaborations with cost-effective communications are considered. Furthermore, in order to alleviate the power scarcity of MHWN, a hybrid fitness function is derived and embedded in the algorithm to extend the overall network lifetime via workload balancing among the collaborative nodes, while still ensuring the arbitrary application deadlines. Simulation results show significant performance improvement in various testing environments over existing mechanisms.

Handling inelastic traffic in wireless sensor networks

The capabilities of sensor networking devices are increasing at a rapid pace. It is therefore not impractical to assume that future sensing operations will involve real time (inelastic) traffic, such as audio and video surveillance, which have strict bandwidth constraints. This in turn implies that future sensor networks will have to cater for a mix of elastic (having no bandwidth constraint requirements) and inelastic traffic. Current state of the art rate control protocols for wireless sensor networks, are however designed with focus on elastic traffic. In this work, by adapting a recently developed theory of utilityproportional rate control for wired networks to a wireless setting, and combining it with a stochastic optimization framework that results in an elegant queue backpressure-based algorithm, we have designed the first-ever rate control protocol that can efficiently handle a mix of elastic and inelastic traffic in a wireless sensor network. We implement this novel protocol in a real world sensor network stack, the TinyOS-2.x communication stack for IEEE 802.15.4 radios and evaluate the real-world performance of this protocol through comprehensive experiments on 20 and 40-node subnetworks of USCs 94-node Tutornet wireless sensor network testbed.

Secure rateless deluge: pollution-resistant reprogramming and data dissemination for wireless sensor networks

A network reprogramming protocol is made for updating the firmware of a wireless sensor network (WSN) in situ. For security reasons, every firmware update must be authenticated to prevent an attacker from installing its code in the network. While existing schemes can provide authentication services, they are insufficient for a new generation of network coding-based reprogramming protocols like Rateless Deluge. We propose Secure Rateless Deluge or Sreluge, a secure version of Rateless Deluge that is resistant to pollution attacks (denial-of-service attacks aimed at polluting encoded packets). Sreluge employs a neighbor classification system and a time series forecasting technique to isolate polluters, and a combinatorial technique to decode data packets in the presence of polluters before the isolation is complete. For detecting polluters, Sreluge has zero false negative rate and a negligible false positive rate. TOSSIM simulations and experimental results show that Sreluge is practical.

Utility max-min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max-min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max-min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.

Rate control for heterogeneous wireless sensor networks: Characterization, algorithms and performance

This paper addresses the rate control and resource allocation problem for heterogeneous wireless sensor networks, which consist of diverse node types or modalities such as sensors and actuators, and different tasks or applications. The performance of these applications, either elastic traffic nature (e.g., typical data collection) or inelastic traffic nature (e.g., real-time monitoring and controlling), is modeled as a utility function of the sensor source rate. The traditional rate control approach, which requires the utility function to be strictly concave, is no longer applicable because of the involvement of inelastic traffic. Therefore, we develop a utility framework of rate control for heterogeneous wireless sensor networks with single- and multiple-path routing, and propose utility fair rate control algorithms, that are able to allocate the resources (wireless channel capacity and sensor node energy) efficiently and guarantee the application performance in a utility proportional or max-min fair manner. Furthermore, the optimization and convergence of the algorithm is investigated rigorously as well.

Enhancing responsiveness and scalability for OpenFlow networks via control-message quenching

OpenFlow has been envisioned as a promising approach to next-generation programmable and easy-to-manage networks. However, the inherent heavy switch-controller communications in OpenFlow may throttle controller responsiveness and, ultimately, network scalability. In this paper, we identify that a key cause of this problem lies in flow setup, and propose a Control-Message Quenching (CMQ) scheme to address it. CMQ requires minimal changes to OpenFlow, imposes no overhead on the central controller which is often the performance bottleneck, is lightweight and simple to implement. We show, via worst-case analysis and numerical results, an upper bound of performance improvement that CMQ can achieve, and evaluate the average performance via experiments using a widely-adopted prototyping system. Our experimental results demonstrate considerable enhancement of controller responsiveness and network scalability by using CMQ, with reduced flow setup latency and elevated network throughput.

Energy-efficient data acquisition by adaptive sampling for wireless sensor networks

Wireless sensor networks (WSNs) are well suited for environment monitoring. However, some highly specialized sensors (e.g. hydrological sensors) have high power demand, and without due care, they can exhaust the battery supply quickly. Taking measurements with this kind of sensors can also overwhelm the communication resources by far. One way to reduce the power drawn by these high-demand sensors is adaptive sampling, i.e., to skip sampling when data loss is estimated to be low. Here, we present an adaptive sampling algorithm based on the Box-Jenkins approach in time series analysis. To measure the performance of our algorithms, we use the ratio of the reduction factor to root mean square error (RMSE). The rationale of the metric is that the best algorithm is the algorithm that gives the most reduction in the amount of sampling and yet the the smallest RMSE. For the datasets used in our simulations, our algorithm is capable of reducing the amount of sampling by 24% to 49%. For seven out of eight datasets, our algorithm performs better than the best in the literature so far in terms of the reduction/RMSE ratio.

A simple framework of utility max-min flow control using sliding mode approach

Motivated by the limitations of current optimal flow control approach, we develop a new utility max-min flow control framework using classic sliding mode control. It consists of a source algorithm and a binary congestion feedback mechanism, in which only the sources with the highest utility at each congested link are required to reduce their transmission rates. It can be directly applied to a multi-service network with heterogeneous applications that have different QoS characteristics. The proposed framework achieves the utility max-min fairness among applications efficiently in the sense of low overhead and rapid convergence. Rigorously, the system is proven to be asymptotically stable by means of Lyapunovs theorem.

Application-Oriented Flow Control for Wireless Sensor Networks

This paper addresses flow control and resource allocation problem for heterogeneous wireless sensor networks which consist of diverse sensor types and applications. The performance of these applications, either elastic data collecting or real-time monitoring, is modelled as a utility function of the sensor source rate. The traditional optimal flow control approach, which requires the utility function to be strictly concave, is no longer applicable because of realtime applications involved. Therefore, we propose a new utility-based flow control framework that is able to allocate the resources (wireless channel usage and sensor node energy) efficiently and guarantee the application performance in a utility proportional or max-min fair manner. Furthermore, the optimization and convergence of the algorithm is examined in the paper as well.