Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 3) ROUTING OF BIG DATA WITH ENERGY EFFICIENCY <s> Big data strongly demands a network infrastructure having the capability to efficiently collect, process, cache, share, and deliver the data, instead of simple transmissions. Such network designs show the requirements of energy efficiency, availability, high performance, and data-aware intelligence. To meet these requirements, we adopt the information-centric networking (ICN) approach, where data are retrieved through names and in-network caching is utilized. However, as the typical existing ICN architectures, content centric network (CCN) cannot efficiently utilize the caches for data sharing because of the on-path caching strategy, and network of information (NetInf) demonstrates the resolution latency for data retrievals. To design an efficient and effective ICN architecture for big data sharing, we combine the strong points of CCN and NetInf, where information islands (IOIs) and management plane are utilized for direct data retrieval and global data discovery, respectively. We provide a reference architecture and propose an aggregatable name-based routing (ANBR), which can naturally enable consumers to retrieve the closest copy of information. In this network, each piece of data can be cached at one IOI at most once, which greatly improves the efficiency of cache usages. The consumers first try to retrieve the data in the local IOI, and then try to globally retrieve it from the closest IOI, holding the copy of the data if necessary. We investigate the impact from the key factor, IOI size, to the energy consumption of ANBR. It shows that energy consumption first decreases and then increases as the IOI size increases, and the optimized IOI size can be found for deployment. Furthermore, we study the relation between the optimized IOI size and the average retrieval times for the data. The result shows that the optimized IOI size increases as the average retrieval times increase. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 3) ROUTING OF BIG DATA WITH ENERGY EFFICIENCY <s> Abstract The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 3) ROUTING OF BIG DATA WITH ENERGY EFFICIENCY <s> With the development of the latest technologies and changes in market demand, the wireless multi-sensor system is widely used. These multi-sensors are integrated in a way that produces an overwhelming amount of data, termed as big data. The multi-sensor system creates several challenges, which include getting actual information from big data with high accuracy, increasing processing efficiency, reducing power consumption, providing a reliable route toward destination using minimum bandwidth, and so on. Such shortcomings can be overcome by exploiting some novel techniques, such as clustering, data fusion, and coding schemes. Moreover, data fusion and clustering techniques are proven architectures that are used for efficient data processing; resultant data have less uncertainty, providing energy-aware routing protocols. Because of the limited resources of the multi-sensor system, it is a challenging task to reduce the energy consumption to survive a network for a longer period. Keeping challenges above in view, this paper presents a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system. After the selection of cluster heads and member nodes, the proposed data fusion technique is used for partitioning and processing the data. The proposed scheme efficiently reduces the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. Afterward, the routing technique is provided based on the layered architecture. The proposed layered architecture efficiently minimizes the routing paths toward the base station. Comprehensive analysis is performed on the proposed scheme with state-of-the-art centralized clustering and distributed clustering techniques. From the results, it is shown that the proposed scheme outperforms competitive algorithms in terms of energy consumption, packet loss, and cluster formation. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 3) ROUTING OF BIG DATA WITH ENERGY EFFICIENCY <s> Ride sharing can reduce the number of vehicles in the streets by increasing the occupancy of vehicles, which can facilitate traffic and reduce crashes and the number of needed parking slots. Autonomous vehicles can make ride sharing convenient, popular, and also necessary because of the elimination of the driver effort and the expected high cost of the vehicles. However, the organization of ride sharing requires the users to disclose sensitive detailed information not only on the pick-up/drop-off locations but also on the trip time and route. In this paper, we propose a scheme to organize ride sharing and address the unique privacy issues. Our scheme uses a similarity measurement technique over encrypted data to preserve the privacy of trip data. The ride sharing region is divided into cells and each cell is represented by one bit in a binary vector. Each user should represent trip data as binary vectors and submit the encryptions of the vectors to a server. The server can measure the similarity of the users’ trip data and find users who can share rides without knowing the data. Our analysis has demonstrated that the proposed scheme can organize ride sharing without disclosing private information. We have implemented our scheme using Visual C on a real map and the measurements have confirmed that our scheme is effective when ride sharing becomes popular and the server needs to organize a large number of rides in short time. <s> BIB004
Wireless sensor networks normally produce large amount of data by collecting information from various sensor nodes geographically located in a region or an area. This data is sometimes so large in size that it can easily be termed as big data. Din et al. BIB003 believe that this ''multi-sensor'' BD needs to be processed and routed by introducing a new ''cluster-based'' scheme that also keeps the battery usage level of sensory nodes at minimal. In the proposed approach (figure 5), sensor nodes gather (or sense) the information and forward it to the Base station (BS). This process is known as 'self-organization' where nodes learn about their neighbors through broadcasting. The responsibility of forming clustering group remains with the BS which is shown as 'flat layer design' in figure 5. The cluster head manages the communication with the BS, floods control messages to neighbors, selects cluster members, and chooses the most energy efficient path for routing as shown with steps 3 'cluster layer design' and 4 'cluster member selection'. Thus, all other sensor nodes are exempted from the overhead of optimal route selection. This in turn saves the battery life of senor nodes. Another novelty of the proposed approach is that only those devices form clusters which are at one-hop distance from the BS, thus the directly connected nodes to BS remain ''unclustered''. This also saves energy of sensor nodes. Once the clusters are formed, BD collected by the participating sensors has to be routed (or disseminated) to the desirable destinations. For this, a technique known as ''data fusion (step 5)'' 1 is used, which separates data at various clusters both contextually and structurally. Fusion technique also monitors each cluster head's data processing performance. Results show that the proposed approach has high energy efficiency because; 1) the direct neighbors of BS remain ''unclustered'' which reduces the number of clusters, and 2) limited number of broadcast messages are exchanged only with selected neighbors. We also highlight the important steps of BIB003 in figure 5 . Another related approach with the focus on energy efficient routing for BD networks is presented in BIB001 . The entire focus of the proposed solution roams around utilizing the benefits of a content centric network (CCN)  for BD retrieval and sharing. An aggregatable named based routing (ANBR) algorithm is proposed that searches the closest copy of data for a user's request. Longest prefix matching is used to retrieve the desired copy of data requested by the user. Most of the routing information is stored locally which speeds up the retrieval process. Moreover, the authors claim that ANBR is energy efficient because it employs a hierarchical routing structure and uses longest prefix matching for information retrieval where one prefix can serve as a set of data to reduce the size of a routing table. This energy efficiency claim has been proven via simulation results, however, the authors failed to provide any comparison with any existing approach. Another promising energy efficient routing algorithm is reported in BIB002 with emphasis on BD cloud computing. The authors use extensive mathematical calculations with linear and dynamic approaches to model/calculate the power consumption of the network, network topology, and energy and time required for route computation. The proposed models have been evaluated using real-time statistics of four different routes each having distinct equipment, capacity, and power consumption. The results show that despite having large number of traversed nodes, one of the four routes consumes less BIB004 We detail fusion in Section V. VOLUME 6, 2018 FIGURE 5. Five functional elements proposed in BIB003 . energy than the others. This is due to the difference in capacity and power consumption of the equipment usedin each route.