Energy big data: A survey <s> 4) DATA STORING AND ROUTING <s> We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks. <s> BIB001 </s> Energy big data: A survey <s> 4) DATA STORING AND ROUTING <s> Making effective use of the vast amounts of data gathered by large-scale sensor networks (sensornets) will require scalable, self-organizing, and energy-efficient data dissemination algorithms. For sensornets, where the content of the data is more important than the identity of the node that gathers them, researchers have found it useful to move away from the Internet's point-to-point communication abstraction and instead adopt abstractions that are more data-centric. This approach entails naming the data and using communication abstractions that refer to those names rather than to node network addresses [1,11]. Previous work on data-centric routing has shown it to be an energy-efficient data dissemination method for sensornets [12]. Herein, we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we first define DCS and predict analytically where it outperforms other data dissemination approaches. We then describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordinates, and stores a key-value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data locally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key-value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads we analytically predict, offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile. <s> BIB002
The large amount of data generated by the widespread sensor networks in the smart grid requires scalability, self-organization, routing algorithms and efficient data dissemination. Generally, the content of the data should be more important than the identity of the sensor used to store data BIB002 . Therefore, comparing with the common routing technology, the data-centric storing and routing technologies have been developed more rapidly in recent decades. In the data storing and routing technology, the data is defined and routed referring to their names instead of the storage node's address. The data can be queried for users after defining certain conditions and adopting certain name-based routing algorithms, like the Data-Centric Storage (DCS) and the Greedy Perimeter Stateless Routing (GPSR) algorithm BIB001 . In these algorithms, each data object belongs to an associated key and each working node stores a group of keys. Any node in the system is allowed to locate the storage node for any key and every node can input and output files based on its keys, via a hash-table-like interface. The following example shows the data storage algorithm in information encryption and decryption. • Encrypt2DS: Each terminal user can encrypt information I into a ciphertext C DS with the data storage encryption algorithm, which encrypts with params and the data storage identity DS in the regional cloud nodes. Therefore, this encryption process is denoted as: