Weisheng Si

An overview of Channel Assignment methods for multi-radio multi-channel wireless mesh networks

Channel Assignment (CA) is an active research area due to the proliferating deployments of multi-radio multi-channel wireless mesh networks. This paper presents an in-depth survey of some of the CA approaches in the literature. First, the key design issues for these approaches are identified, laying down the basis for discussion. Second, a classification that captures their essentials is proposed. Third, the different CA approaches are examined individually, with their advantages and limitations highlighted; furthermore, categorical and overall comparisons for them are given in detail, clarifying their sameness and differences. Finally, the future research directions for CA are discussed at length.

RMAC: a reliable multicast MAC protocol for wireless ad hoc networks

This work presents a new MAC protocol called RMAC that supports reliable multicast for wireless ad hoc networks. By utilizing the busy tone mechanism to realize multicast reliability, RMAC has the following three novelties: (1) it uses a variable-length control frame to stipulate an order for the receivers to respond, such that the problem of feedback collision is solved; (2) it extends the traditional usage of busy tone for preventing data frame collisions into the multicast scenario; and (3) it introduces a new usage of busy tone for acknowledging data frames. In addition, we also generalize RMAC into a comprehensive MAC protocol that provides both reliable and unreliable services for all the three modes of communications: unicast, multicast, and broadcast. Our evaluation shows that RMAC achieves high reliability with very limited overhead. We also compare RMAC with other reliable multicast MAC protocols, showing that RMAC not only provides higher reliability but also involves lower cost.

A greedy model with small world for improving the robustness of heterogeneous Internet of Things

Robustness is an important and challenging issue in the Internet of Things (IoT), which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary. The small world model has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency.

ROSE: Robustness Strategy for Scale-Free Wireless Sensor Networks

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.

Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers

Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.

Reliability and energy efficiency in cloud computing systems

With the popularity of cloud computing, it has become crucial to provide on-demand services dynamically according to the users requirements. Reliability and energy efficiency are two key challenges in cloud computing systems (CCS) that need careful attention and investigation. The recent survey articles are either focused on the reliability techniques or energy efficiency methods in cloud computing. This paper presents a thorough review of existing techniques for reliability and energy efficiency and their trade-off in cloud computing. We also discuss the classifications on resource failures, fault tolerance mechanisms and energy management mechanisms in cloud systems. Moreover, various challenges and research gaps in trade-off between reliability and energy efficiency are identified for future research and developments.

A distributed energy saving approach for Ethernet switches in data centers

With popularity of data centers, energy efficiency of Ethernet switches in them is becoming a critical issue. Most existing energy saving approaches use a centralized methodology that assumes global knowledge of data center networks. Though these approaches can achieve nearly optimal energy saving for static traffic patterns, they are not suitable when the traffic patterns can change rapidly or the data centers have a large size. To overcome these limitations, this paper proposes a novel distributed approach called eAware that dynamically idles a port or a switch to save energy by examining the queue lengths and utilizations at switch ports. Through extensive simulations in ns-2, we compare eAware with an existing energy oblivious approach, showing that eAware can save 30%-50% on the total energy consumption by switches in data centers, and only increases the average end-to-end delay of packets by 3%-20% and the packet loss ratio by 0%-0.9%.

A Geometric Deployment and Routing Scheme for Directional Wireless Mesh Networks

This paper first envisions the advent of the wireless mesh networks with multiple radios and directional antennas in future. Then, based on the observation that simplicity induces efficiency and scalability, the paper proposes a joint geometric deployment and routing strategy for such mesh networks, and also gives a concrete approach under this strategy. The main idea of this strategy is to deploy mesh networks in certain kind of geometric graph, and then design a geometric routing protocol by exploiting the routing properties of this graph. The proposed concrete approach comprises two parts: (1) a topology generation algorithm based on Delaunay triangulations and (2) a geometric routing protocol based on the greedy forwarding algorithm. Both parts are characterized by simplicity and appealing properties, with formal proofs provided when possible. The simulation results validate our proposed approach.

New Memoryless Online Routing Algorithms for Delaunay Triangulations

Memoryless online routing (MOR) algorithms are suitable for the applications only using local information to find paths, and Delaunay triangulations (DTs) are the class of geometric graphs widely proposed as network topologies. Motivated by these two facts, this paper reports a variety of new MOR algorithms that work for Delaunay triangulations, thus greatly enriching the family of such algorithms. This paper also evaluates and compares these new algorithms with three existing MOR algorithms. The experimental results shed light on their performance in terms of both Euclidean and link metrics, and also reveal certain properties of Delaunay triangulations. Finally, this paper poses three open problems, with their importance explained.

RAMSES: A new reference architecture for self-adaptive middleware in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSNs) consist of networks composed of tiny devices equipped with sensing, processing, storage, and wireless communication capabilities. WSN nodes have limited computing resources and are usually powered by batteries. First generations of WSNs were designed to attend requirements of a unique target application usually with a single user, who was also the infrastructure owner. However, the rapid evolution in this area and the increasing of the complexity of the sensors and applications pose new challenges to WSN solutions, which can be addressed by specific middleware platforms for these networks. Existing middleware systems provide suitable mechanisms to define the high-level application logic and to deal with heterogeneity and distribution issues of WSN, but most of them do not provide explicit mechanisms to define the underlying autonomic behavior of the system, an essential feature of this kind of network. In this perspective, Autonomic Computing (AC) appears as a promising option to meet autonomic requirements in WSN middleware design. This paper presents the consolidated specification of RAMSES, a reference architecture of a self-adaptive middleware for WSNs. RAMSES was conceived in light of a well-stablished Reference Architecture Model, the RAModel. It follows the autonomic computing model MAPE-K, and presents a mapping of AC conceptual model to a set of software components. We claim that, with the aid of a middleware that supports the autonomic computing principles, a WSN becomes an autonomous WSN by design . RAMSES realizes our vision by providing: (i) an architectural template with core aspects of the self-adaptive systems from which is possible to build concrete middleware instances for self-adaptive WSNs, and (ii) a specification of the reference architecture using a formal architecture description language (Pi-ADL), which enables the representation of dynamic software architectures as required by WSNs. A scenario-based qualitative analysis and a checklist survey conducted with experts demonstrate the effectiveness of RAMSES. Moreover, a concrete WSN middleware instance derived from RAMSES was implemented as a proof of concept.