To help researchers identify uncharacterized human genes that can be investigated using Drosophila melanogaster as a model organism, a new query federated between neXtProt and OrthoDB has been added on the neXtProt platform (NXQ_00300). The output of this query shows that there are 44 uncharacterized genes highly expressed in the human brain for which a homolog is found in Drosophila melanogaster

In this paper, we show that every Schnyder drawing is a greedy embedding. Schnyder drawings are used to represent planar (maximal) graphs. It is a way of getting coordinates in R2 given a graph G=(V,E) such that the representation is planar. The Schnyder technique leads to a family of representations and previous results show that a particular representation may be chosen such that the drawing has additional properties like being greedy or monotone. In this article, we relax the definition of greediness to a definition that does not rely on the geometry and the Euclidean distance in R2, but rather on the combinatorial graph G. The construction of greedy paths valid for all Schnyder representations shows that, provided the relaxed definition, every Schnyder drawing is a greedy embedding.

The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

Geometric routing is a routing scheme proposed for networks with highly dynamic topology, like wireless ad hoc networks. It uses the geometric coordinates of the nodes and makes routing decisions based on the geometric properties between them. Hence, it does not build and maintain routing tables. Nevertheless, as geometric routing requires an auxiliary location service to equip nodes with geometric location, there are not much deployments in real network settings. In this chapter, we investigate an efficient localization system called Virtual Raw Anchor Coordinates (VRACs), which is an anchor-based coordinate system. It assigns the raw distances from anchors as the coordinates of nodes, hence avoiding further computations. Despite its efficiency, it is not possible to perform geometric operations on VRAC. In this manuscript, we propose alternative constructs to perform geometric routing over VRAC. Initially, a greedy routing algorithm is developed, which is then combined with a face routing strategy, when greedy routing does not guarantee delivery. Moreover, we present the conditions over which greedy routing can be performed on VRAC. In Sect. 5, a geometric routing algorithm is presented, where greedy and face routing are combined to guarantee the delivery of messages. In Sect. 6, a greedy routing algorithm is presented and proved to be successful given that certain connectivity conditions are satisfied.

Geographic routing is an appealing routing strategy that uses the location information of the nodes to route the data. This technique uses only local information of the communication graph topology and does not require computational effort to build routing table or equivalent data structures. A particularly efficient implementation of this paradigm is greedy routing, where along the data path the nodes forward the data to a neighboring node that is closer to the destination. The decreasing distance to the destination implies the success of the routing scheme. A related problem is to consider an abstract graph and decide whether there exists an embedding of the graph in a metric space, called a greedy embedding, such that greedy routing guarantees the delivery of the data. A common approach to assign geographic coordinates is to measure distances (for instance the distances between neighboring nodes) and compute(virtual) coordinates. The rationale of the Virtual raw Anchor Coordinate System(VRAC) is to use the (raw) measured distances as coordinates in order to avoid further computations. More precisely, each node needs to measure three distances. In this paper, we investigate the existence of greedy routing in the VRAC coordinate system using a metric free characterization of greedy paths that is more general than in previous works. We show that if the graph is saturated (see definition in the text) then the greedy algorithm guarantees delivery. Interestingly, the approach of greediness here applies to Schnyder drawings of planar triangulations. Indeed, by choosing the measured distances appropriately Schnyder drawings of planar triangulations are always saturated and hence our greedy routing algorithm succeeds. The VRAC coordinates have conditions to satisfy to make greedy routing successful. These conditions can be inferred from geometric considerations. However, we formulate these conditions in an abstract way in order to avoid geometric considerations and in order to make possible further derivation of virtual VRAC coordinate systems, i.e. using only the abstract graph description. In particular using only local information would lead to distributed algorithm.

Greedy routing is an appealing routing mechanism, which does not require to build and maintain routing tables. Although, it requires a specific geometric coordinate assignment for underlying network nodes. Such a coordinate assignment is called a greedy embedding, where messages are routed over a distance decreasing path from the source to the destination. Despite various research on computation of greedy embeddings, robustness of greedy coordinates in dynamic topologies has not investigated thoroughly. In this paper, we propose Greedy Zone Routing (GZR), an alternative routing architecture, which constructs a greedy embedding of a logical network graph, namely the zone graph. Distinctively, GZR assigns greedy coordinates to each zone, as oppose to individual nodes. Messages are routed in two levels: greedy geographic routing is performed between zones and classical tree-based routing is performed within a zone. This way, trees have a manageable sizes as their depths are limited by the diameter of the zones. Greedy zone routing eliminates the need of re-computing the coordinates on changes in the network topology, hence being efficient in terms of the protocol overhead. Our simulations demonstrate that, GZR produces routes with low stretch and required to maintain small routing tables, while accounting to 50% less control overheads compared to a state of the art greedy routing protocol.

Geographic routing is an appealing routing strategy that uses the location information of the nodes to route the data. This technique uses only local information of the communication graph topology and does not require computational effort to build routing table or equivalent data structures. A particularly efficient implementation of this paradigm is greedy routing, where along the data path the nodes forward the data to a neighboring node that is closer to the destination. The decreasing distance to the destination implies the success of the routing scheme. A related problem is to consider an abstract graph and decide whether there exists an embedding of the graph in a metric space, called a greedy embedding, such that greedy routing guarantees the delivery of the data. In the present paper, we use a metric-free definition of greedy path and we show that greedy routing is successful on planar triangulations without considering the existence of greedy embedding. Our algorithm rely entirely on the combinatorial description of the graph structure and the coordinate system requires O(log(n)) bits where n is the number of nodes in the graph. Previous works on greedy routing make use of the embedding to route the data. In particular, in our framework, it is known that there exists an embedding of planar triangulations such that greedy routing guarantees the delivery of data. The result presented in this article leads to the question whether the success of (any) greedy routing strategy is always coupled with the existence of a greedy embedding?

In this paper, we propose Greedy Zone Routing, an alternative routing architecture for wireless ad-hoc networks. Greedy Zone Routing partitions the network into zones, which are assigned geographic coordinates. Messages are routed in two levels, where in the zone level greedy geographic routing is performed between zones. Routing within a zone is performed based on the local routing trees. We demonstrate that our protocol outperforms a classical tree based routing scheme, where RPL routing protocol is based upon. We believe that our protocol can be adapted into a tree based protocol like RPL for improved scalability and performance.

In this paper we present a novel combinatorial approach for geographic routing with delivery guarantees. Proposed algorithm can be seen as a variant of GFG (Greedy Face Greedy of Bose et.al) algorithm, but based on the defined combinatorial properties of the graph. We utilize a distributed planarization algorithm of a geometric graph, which is based on the Schnyder's characterization of planar graphs. The new approach is combinatorial in the sense that the nodes are ordered with respect to three distinct order relations satisfying the suitable properties. The coordinate system motivated the development of this routing algorithm is VRAC (Virtual Raw Anchor Coordinates), which localizes nodes based on the raw distances from three fixed anchors. Since the positions of the anchors need not to be known, the nodes localized by the VRAC coordinate system does not correspond to the exact geographic location of nodes, yet leaving sufficient information to define necessary combinatorial constructs.

Smart environments are places where different kind of embedded devices are interconnected in order to provide their occupants intelligent services improving their comfort and convenience. These smart environments are seen to be important for the future urban ecosystems in terms of user friendliness, quality of life, energy efficiency and sustainability. Lately such environments have become economically and technologically feasible due to the advancements in embedded and distributed technologies. Most of the novel infrastructures adopt smart technologies, while old infrastructures need a transition towards smart environments. Even though different technologies and devices are available, there is a need for an appropriate methodology and a system architecture for a smooth and profitable transition towards smart environments. In this paper we present a framework for creating personalized smart environments using wireless sensor networks. This framework, among other services that it provides, is able to identify people and take personalized actions such as control electrical devices based on their preferences and needs. We present, as a proof of concept, a real world deployment where two scenarios are implemented in two office premises.

As we are moving towards to the Internet of Things (IoT) era, Wireless Sensor Networks (WSN) in smart buildings delineate the heart of such systems' architecture. WSN systems are mature enough to support the IoT vision and different architectural designs and communication protocols are developed to realize this vision. In this paper, two different WSN architectural approaches for smart building systems are presented. In the first one, IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) deployment is used, which is designed specifically for constrained embedded devices. In the second one, the system is developed without the usage of IP. To evaluate these two approaches we implemented a scenario of a smart building environment on top of them. We analyze and compare them, both from theoretical and practical point of view. Finally, as a proof of concept we evaluated them experimentaly in our testbed and we reported our conclusions.

Geographic routing based on virtual coordinates has been studied extensively, especially in environments expensive localization techniques are infeasible. Even though, the construction of virtual coordinate system is theoretically understood, their practical deployment is questionable due to computational requirements. An alternative approach is to use raw range measures from a special set of nodes called "anchors" as virtual coordinates, which only preserve partial geographic knowledge. In this paper we follow a similar approach, but focus on answering the question "what are the minimal geometric primitives required to perform geometric routing?". We take the first step towards answering this question, based on a node centric local geometric view of localized nodes. We define local geometric primitives and show that geographic face routing can be performed with those primitives.

Network coding is a novel concept for improving network capacity. This additional capacity may be used to increase throughput or reliability. Also in wireless networks, network coding has been proposed as a method for improving communication. We present our experience from two studies of applying network coding in realistic wireless sensor networks scenarios. As we show, network coding is not as useful in practical deployments as earlier theoretical work suggested. We discuss limitations and future opportunities for network coding in sensor networks.

Network coding is a novel concept for improving network capacity. This additional capacity may be used to increase throughput or reliability. Also in wireless networks, network coding has been proposed as a method for improving communication. We present our experience from two studies of applying network coding in realistic wireless sensor networks scenarios. As we show, network coding is not as useful in practical deployments as earlier theoretical work suggested. We discuss limitations and future opportunities for network coding in sensor networks. 1 Network Coding in Wireless Sensor Networks Network Coding was introduced by Ahlswede et al. [1], proving that it can increase multicast capacity. Since then, it has been investigated in several different networked scenarios which demand different traffic characteristics. Most previous research has focused on theoretical aspects of applying network coding to sensor networks. There are, however, also more practical examples of applying network coding in wireless networks. Network coding has, for example, demonstrated its usefulness in the general networking domain (e.g., Katti et al. [2]). Hou et al. have proposed AdapCode [3], a network-coded variant of the Deluge protocol [4]. Aoun et al. specifically target real-time wireless sensor networks [5]. They use network coding together with packet skipping to improve on-time goodput in real-time WSNs. Their topology, however, is not applicable to data collection. While network coding for data dissemination can increase reliability, it is harder to apply network coding for convergecast, probably the most important traffic paradigm in wireless sensor networks where data is collected from multiple sources and transported to one or more data sinks. We have worked on two efforts to apply network coding in wireless sensor networks. In the first study, we have applied network coding to extend GinMAC, a state-of-the-art MAC protocol [6]. In the second, we have tried to apply network coding for reliable and low delay communication in network with very high packet loss rates [7]. In the second case it has turned out that network coding cannot live up to the expectations while in the first case in all scenarios we studied, network 2 Voigt, Roedig, Landsiedel, Samarasinghe, Bogadi SP (a) Schedules without NC (0 and 1 retransmissions) (b) Schedule with NC Fig. 1: Schedules with and without Network Coding (NC). NC compresses the schedule while still enabling retransmissions. coding can be replaced with simple overhearing and the retransmission of the lost packets which avoids the complexity of network coding. In the remainder of this paper, we detail our studies and discuss limitations and usefulness of network coding in sensor networks. 2 Network Coding in GinMAC We have applied network coding in GinMAC [6], the MAC layer of the GINSENG system designed for industrial monitoring and control. In industrial process automation and control networks it is common that many nodes monitoring the same production process and are hence deployed in close proximity to each other. Thus, most nodes are in interference range of all other nodes in the network and many nodes are also capable of overhearing packets. GinMAC is a single channel state of the art TDMA-based MAC layer for performance-controlled sensor networks [8]. GinMAC uses an offline process to dimension a network before deployment. The heart of the dimensioned network is a TDMA schedule with an epoch length consisting of E slots. There are three types of slots, namely basic slots for transmission (TX) and reception (RX), additional slots for retransmissions (RTX) to increase reliability and unused slots to decrease the duty cycle. The schedule determines latency, power consumption and reliability. The latter is increased by adding more retransmission slots. We employ overhearing as a mechanism for tuning network reliability and latency. Due to external interference a node may not be able to deliver a packet to a parent node. However, a neighbouring node may be able to overhear the packet and then try to deliver the packet on its behalf. Thus, instead of the sender retransmitting packets, neighbouring nodes that overhear the initial transmission take care of the retransmission. Obviously, a node that overhears packets will now need capacity to transmit these. In order to avoid allocation of additional transmission slots we propose network coding to combine several overheard packets into a single retransmission. Thus, we use overhearing and network coding for retransmissions reducing the overall number of transmission slots. Figure 1 shows the resulting schedules with and without network coding for a binary tree with the leave nodes A and B. In the example node A has the first transmission slot. With network coding node B overhears A’s transmission in this transmission slot. Thereafter, node B transmits its own packet in its Practical Network Coding in Sensor Networks: Quo Vadis? 3 allocated slot. Finally, node B uses the retransmission slot in which it transmits a network coded packet that contains the information of the packet transmitted by A and B. For example, packets from A and B can be combined using simple binary XOR coding. The root node will receive A and B’s packets if one original transmission is received and the network coded retransmission is received (both packets are also received if both original packet transmissions are received). As a result we have now constructed a schedule in which both nodes A and B have a way to retransmit a packet despite that we provision only one retransmission slot. Thus, with network coding less slots are provisioned while we still provide a retransmission option for all nodes. The slight drop in reliability compared to the original scheme with one retransmission may be well acceptable for some application scenarios since a gain in transmission latency is achieved as the schedule is compressed. Figure 1 shows that the schedule compression for the binary tree is 3 : 4 (0.75). In our previous work we have shown that we achieve approximately similar error packet reception rates for GinMAC with and without network coding and that for high packet loss rates we can also achieve a reduction in power consumption [6]. 3 Network Coding for Reliable, Low-delay Communication Many sensor network applications, in particular sensor actuator networks, require a certain delay and reliability to enable control loops on top of a wireless sensor network. Network coding promises highly reliable communication. We discuss our lessons learned from integrating it into a collection tree application [7]. While network coding increased reliability in some scenarios, we noticed the following two key limitations: (1) strongly increased delay, and (2) high overhead due to limited lack of adaptability: Delay: Commonly, WSN applications operate at low data rates: often, a node generates packets in the order minutes and the whole network traffic is in the order of a couple of packets per second. However, to achieve a robust interleaving and mixing of packets for network coding, one commonly applies a coding scheme across multiple packets. Thus, nodes have to wait for these packets to accumulate, which significantly increases delay. For example, in today’s collection protocols such as CTP [9] or Contiki Collect [10], one sees typical network delays in the order of milliseconds. However, waiting for enough packets for coding to accumulate takes a couple of seconds. Thus, the delay added by network coding is several order of magnitude higher than the network delay. Hence, network coding seems only beneficial when the applications do not have delay constraints. Overhead and lack of adaptability: As network coding is an end-to-end scheme, its feedback is limited when compared to per-hop acknowledgments. Thus, it is difficult for the source to adjust its coding rate to changing network conditions. Reflecting this, we had to employ a redundancy level in our coding scheme that matches the maximum expected loss rate. As a result, this approach 4 Voigt, Roedig, Landsiedel, Samarasinghe, Bogadi SP adds a large overhead compared to the average scenario. Especially, when keeping in mind, that links in WSNs are often bursty [11], leading to high, short-term packet losses. 4 Limitations and Applicability of Network Coding In the previous section we have already outlined some limitations of applying network coding for data collection. While our results with GinMAC (see Section 2) are encouraging, we later realized that we do not need network coding in this scenario. If either packet A or packet B is lost, we can simply retransmit the lost packet. If both packets A and B are lost, then network coding does not help either and it is better to retransmit only one of the lost packets. We have not found larger topologies where we could use network coding while it was not possible to do the same with overhearing and standard packet retransmissions only. For the single-sink scenario, we have found one use of network coding: when we do not apply acknowledgements which might be useful when the schedule needs to be compressed to a minimum but still some retransmissions should be enabled. In particular for trees where each node can have more than one sibling (e.g., ternary trees), network coding may be useful when acknowledgements are not enabled. Furthermore, in multi-sink scenarios, similar to the original network coding scenarios, we expect network coding to be beneficial.

Network Coding as a recently emerged concept in Communication Networks Research, has attracted a lot of attention in networking research community. Previous research on applying Network Coding in Wireless Sensor Networks, do not consider most crucial constraints inherent to sensor networks. Especially most of them assume overhearing of neighboring transmissions for free, which is unrealistic in practical sensor network environments where nodes have limited energy. Therefore these applications fail to make an impact on real world sensor network deployments. In some sensor networking scenarios it is possible to manipulate overhearing in a controlled manner. In this research we apply network coding in to improve throughput of a Time Division Multiple Access(TDMA) based Medium Access Control(MAC) protocol called GINMAC , manipulating overhearing appropriately. Our results show that network coding can improve the throughput while trading owith other network performance characteristics like reliability and power consumption.

The two key benefits of network coding are increased reliability and throughput. Most network coding approaches for wireless networks rely on overhearing neighboring transmissions. Overhearing in sensor networks, however, is not energy-efficient. In this paper, we extend GinMAC, a state-ofthe-art MAC protocol, applying network coding with limited overhearing. Our approach reduces the delay allocating less retransmission slots. Our results show that network coding with limited overhearing reduces the power consumption of GinMAC while maintaining the desired level of reliability.