Interconnecting wireless sensor and wireless mesh networks: challenges and strategies

Wireless sensor networks consist of several hundredths of simple sensing devices, equipped with a radio. They are typically used for monitoring and automation purposes of large areas. Due to their simplicity, these networks quickly run out of energy, and often have problems regarding scalability and available bandwidth. To solve these issues, current research is mostly limited to the addition of extra sinks to the network, or the use of gateways to request sensor data over the Internet. In this paper, we explore how wireless sensor networks can be combined with wireless mesh networks to obtain a more optimized solution. The mesh network can be used to connect separate sensor networks, to connect sensor nodes with a monitoring platform, or as a scalable backbone for sensor to sensor communication. Additionally, we give an overview of the advantages and disadvantages of existing interconnection techniques between wireless and mesh networks, and propose several new interconnection strategies. Finally, we identify remaining challenges, upon which future research can be based

Design and implementation of a generic connection management and service level agreement monitoring platform supporting the virtual private network service

C

Comparative study of peer-to-peer architectures for scalable resource discovery

Resource discovery is an important aspect of many modern large-scale distributed systems. In the past, this problem has been solved using many different approaches, such as a central registry server, flooding-based protocols, and distributed hash tables. In this paper, these three widely used architectures are compared, using measurement results obtained from real implementations run on an Emulab emulation environment. This allows us to study the advantages and disadvantages of the architectures and determine their usefulness. The measurement study lead to several interesting conclusions. First, the centralised architecture incurs the least traffic overhead. However, it balances the load poorly, and introduces a single point-of-failure. Second, of the two decentralised architectures, the distributed hash table generates the least overhead. Finally, hierarchical architectures were shown to be most effective when the fraction of super-peers compared to regular peers is small.Resource discovery is an important aspect of many modern large-scale distributed systems. In the past, this problem has been solved using many different approaches, such as a central registry server, flooding-based protocols, and distributed hash tables. In this paper, these three widely used architectures are compared, using measurement results obtained from real implementations run on an Emulab emulation environment. This allows us to study the advantages and disadvantages of the architectures and determine their usefulness. The measurement study lead to several interesting conclusions. First, the centralised architecture incurs the least traffic overhead. However, it balances the load poorly, and introduces a single point-of-failure. Second, of the two decentralised architectures, the distributed hash table generates the least overhead. Finally, hierarchical architectures were shown to be most effective when the fraction of super-peers compared to regular peers is small.C

Design of grids for mobile thin client computing

B

Providing fault-tolerance in unreliable grid systems through adaptive checkpointing and replication

As grids typically consist of autonomously managed subsystems with strongly varying resources, fault-tolerance forms an important aspect of the scheduling process of applications. Two well-known techniques for providing fault-tolerance in grids are periodic task checkpointing and replication. Both techniques mitigate the amount of work lost due to changing system availability but can introduce significant runtime overhead. The latter largely depends on the length of checkpointing interval and the chosen number of replicas, respectively. This paper presents a dynamic scheduling algorithm that switches between periodic checkpointing and replication to exploit the advantages of both techniques and to reduce the overhead. Furthermore, several novel heuristics are discussed that perform on-line adaptive tuning of the checkpointing period based on historical information on resource behavior. Simulation-based comparison of the proposed combined algorithm versus traditional strategies based on checkpointing and replication only, suggests significant reduction of average task makespan for systems with varying load.As grids typically consist of autonomously managed subsystems with strongly varying resources, fault-tolerance forms an important aspect of the scheduling process of applications. Two well-known techniques for providing fault-tolerance in grids are periodic task checkpointing and replication. Both techniques mitigate the amount of work lost due to changing system availability but can introduce significant runtime overhead. The latter largely depends on the length of checkpointing interval and the chosen number of replicas, respectively. This paper presents a dynamic scheduling algorithm that switches between periodic checkpointing and replication to exploit the advantages of both techniques and to reduce the overhead. Furthermore, several novel heuristics are discussed that perform on-line adaptive tuning of the checkpointing period based on historical information on resource behavior. Simulation-based comparison of the proposed combined algorithm versus traditional strategies based on checkpointing and replication only, suggests significant reduction of average task makespan for systems with varying load.P