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Enhancing DSR maintenance with power awareness
Computer Standards & Interfaces 35 (2013) 107–113
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Computer Standards & Interfaces journal homepage: www.elsevier.com/locate/csi
Enhancing DSR maintenance with power awareness Giampaolo Bella a,⁎, Gianpiero Costantino b, Jon Crowcroft c, Salvatore Riccobene a a b c
Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy IIT-CNR, Pisa, Italy Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
a r t i c l e
i n f o
Article history: Received 15 September 2011 Received in revised form 25 February 2012 Accepted 28 June 2012 Available online 14 July 2012 Keywords: Power saving Wireless network Mobile device Collaboration
a b s t r a c t A power-aware route maintenance protocol for Mobile Ad Hoc Networks (MANETs) is introduced. Termed Dynamic Path Switching (DPS), the new protocol puts an overloaded node to sleep before a route link breaks because that node runs out of energy, and brings other suitable nodes into play instead. When the battery charge of a node reaches a stated level, the node can advance a request to change to a sleep state for a while. The request is honoured unless survival of some path rests on the forwarding activity of that very node. All nodes are assumed to be collaborative. The DPS protocol is fully backward compatible, as it can be implemented within existing routing protocols such as Dynamic Source Routing (DSR). The new protocol has been extensively simulated with the established network simulator NS2. The findings indicate a much improved power awareness of the updated routing protocol with respect to the unadorned one. Power saving is particularly effective during long-lived sessions. © 2012 Elsevier B.V. All rights reserved.
1. Introduction If we lived in a world where energy was infinite, we would not have to worry about battery-life in our mobile devices. Unfortunately, reality is different and, for example, the smartphone industry finds battery life a seriously limiting constraint. In particular, the number and range of applications requiring a lot of battery power is increasing rapidly. While research to increase battery life is making progress, mobile device makers are investigating a number of techniques to preserve battery power. Just a few years back, it was not rare to enforce computational limitations exactly to save battery power, as with the first generation of iPhones [1], which only came with EDGE connectivity rather than 3G. EDGE is slower than 3G but uses less battery power, as shown by Balasubramanian et al. [2]. Although it remains uncertain whether power saving was the real motivation for such an important technological hence commercial choice, Apple publicly claimed 3G hardware to be hungry of battery power. It was only a year later when Apple introduced iPhone 3G, though still continuing to thwart battery consumption by enforcing computational limitations. It may seem emblematic that multitasking did not arrive until the fourth version of their OS. It is clear that the battery power of mobile nodes is a limited resource, and there is no way to allow a device to live forever, hence techniques to maximise battery life are relevant. Our research focuses ⁎ Corresponding author. E-mail addresses: [email protected] (G. Bella), [email protected] (G. Costantino), [email protected] (J. Crowcroft), [email protected] (S. Riccobene). 0920-5489/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.csi.2012.06.007
on power saving within Mobile Ad Hoc Networks (MANETs). Here, only mobile devices, such as smartphones or PDAs, can take part. No infrastructure is provided, and when a new node (also addressed as device below) joins the MANET, it is ready to communicate both with and on behalf of other nodes. Message forwarding is in fact carried out by participants in the network and no dedicated routers are required. Mobility may also influence the role that nodes take in a network. However, because mobility is usually within a bounded space, the selected path between two end nodes is likely to remain unvaried. Traffic between the two end nodes will then involve a constant set of intermediary nodes acting as packet forwarders. For example, a device in a crowded location may have its resources called upon frequently, if the routing protocol chooses it for long time. The forwarders' persistent activity dramatically consumes their batteries, increasing the risks of flattening them. Although routes are often built on demand in order to get the most reliable path at a given moment, the path selected may not be best in terms of remaining battery power of the devices that form it. Hence, nodes in a crucial network position may deplete their battery rapidly because they may have to forward vast traffic. This is a severe limitation. We introduce the Dynamic Path Switching (DPS) route maintenance protocol to allow the nodes that are stressed persistently, and hence strain their batteries, to call for the selection of a new route. DPS is therefore power-aware. In particular, when a node has been forwarding packets for a long time, it may request to quit its role in order to preserve battery. If it is determined that the right conditions are respected, then the device will suspend its activity. Hence, a node will less likely run out of battery. It is assumed that all nodes comply with the routing protocol, hence no node will suspend its activity
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arbitrarily — no node will take a selfish behaviour and save battery improperly. We modified the Dynamic Source Routing (DSR) [3] protocol extending it with DPS. The new routing protocol was evaluated using the Network Simulator 2 (NS2) [4] through extensive simulations. The findings will be comparatively presented below over the power-aware DPS and the power-unaware DSR, confirming the benefits of the former, especially in extensive communications. The presentation begins with a survey of other recent research related to power saving (Section 2). Then, it provides an overview of the key features of the Dynamic Source Routing protocol (Section 3). After that, it introduces our routing protocol (Section 4), and our simulation results (Section 5). The paper terminates with conclusions and discussions of potential future work (Section 6). 2. Related work Researchers are currently investigating several areas related to power saving. Tseng et al. list them as transmission power control, power-aware routing and low-power mode [5]. The first area features research efforts towards avoiding collision or minimising battery consumption during transmission. There are various procedures to allow nodes to save battery while keeping the network connected [6,7]. Even during packet transmission, each node can balance the transmission strength to its battery level. Using the same approach of tuning the battery consumption during transmission, Wang proposes a technique to calculate the precise level of battery power to spend in communications [8]. Nodes compute it during the route discovery process run by a source. When a route request is sent along a path, the setup packet stores the relevant information. This is then used to compute the power needed to forward a packet hop-by-hop. The procedure is repeated by all nodes that forward the request. Finally, path setup is reflected back by the receiver, carrying all the information gathered in the forward path before. From now on, the source can choose the path where the total residual battery power of nodes is at its maximum. Power-aware routing is the second area of research in power saving. Kim and Koo advance a power-aware routing [9]. Their protocol modifies the original link between nodes reducing the energy consumption. By allowing the use of more hops, if necessary, a node can send a packet to a neighbour closer to the destination, reducing the power required for forwarding. PARO and ZRP name the two procedures that implement this approach. Tao and Luo introduce an extension to DSR to make the protocol power-aware in [10]. By using all routes found using DSR, they introduce a function called dynamic priority-weight, which is used to compare in terms of required battery power all paths that are found. The one that requires least power will be the candidate for end-to-end communication. Maleki et al. in [11] advance a new approach to increase nodes' lifetime by means of min-cost routing. They modify the protocol for DSR-Route Discovery and DSR-Route Maintenance as follows: a destination node, which receives different paths from the RREQ sent by a sender, is able to select the route that costs less. However, a node that is involved in heavy-forwarding may opt for disconnection to preserve its energy. This will break all paths that traverse it, raising the risk of network partitioning. Also, the protocol is not backward compatible because all participating nodes must conform to it. The authors of [12] propose a combination of two strategies to reduce the battery consumption of nodes. They consider both the residual energy of a node and the current request of energy due to the tasks of the node. The latter analysis is done by monitoring the activity of the node, such as the number of packets in its queue. By means of this study, it is possible to have an estimation of the nodes' lifetime, and they can be discarded when their energy is insufficient. A comparative analysis of different energy-aware protocols was proposed by Safwat et al. in [13]. They implemented a simulator that studies the energy consumption from
the physical and MAC layer. The routing-aware protocols compared by the authors are: minimum battery cost routing (MBCR) [14], min– max battery cost routing (MMBCR) [14], maximum battery life routing (MTPR) [15], and, finally, the combination of the last two protocols (CMMBCR). Findings show how these protocols gave different results when the network's mobility changes. A comparative study of the aforementioned protocols was done by Cano and Kim in [16]. Also, a survey of existing energy-aware protocols is illustrated in [17]. It organises the existing literature into different categories: active and passive energy savings, network lifetime maximising protocols, topology control protocols, energy-efficient multicasting and broadcasting protocols. Each work is explained in depth, while advantages and disadvantages are highlighted. Two works are most representative of the low-power mode area. Wang et al. develop another protocol to preserve battery in [18]. They make use of global synchronisation [19] to coordinate beacon intervals between devices. This is done by using a pre-chosen hash function with the MAC-address of the receiver. In fact, that unique value is used as input for the hash function and the result makes it easier to understand the listening period of a neighbour. This process is reiterated until the packet arrives at the final node. Another solution based on different states of a node is due to Tseng et al. [5]. They implement three distinct protocols to keep a node in power-saving mode as long as possible though still maintaining network connectivity. The focus of the present paper can be seen as lying between low-power mode and power-aware routing. The main requirement is to maximise backward compatibility with the original DSR protocol. Hence, the original route discovery protocol of DSR, contrarily to previous work such as [11], is kept unmodified. The goal of this paper is an extended maintenance protocol aimed at dynamically favouring survival of nodes that are in a critical energy condition. The new maintenance protocol allows each node that is low on energy to seek permission to sleep from anyone in its chain of forwarders, while preserving connectivity. The only requirement on the called node is DPS compliance. Remarkably, for this protocol to work, it is sufficient that at least one among the forwarders is DPS-compliant. Our contribution is more geared towards individual survival than towards overall power consumption throughout a route. This is motivated by scenarios where nodes are collaborative but do not want to die. Intermediate scenarios appear to be worthy of further investigation. 3. Dynamic source routing Routing protocols of MANETs fall into two main families. The first are proactive protocols, which use a table-driven approach to establish paths, hence the reliability of routing paths depends on table refreshing. If this is done too often, control traffic can overload connection links. On the contrary, reactive protocols build paths on-demand and Dynamic Source Routing (DSR) is among the best known. We adopt it here as our running example, and outline it briefly below, while for a complete description we refer the reader to the original paper [3]. As other routing protocol, DSR employs flooding to discover paths (Section 3.1). Then, to manage situations in which current routes are broken, DSR implements a separate route maintenance procedure (Section 3.2). Finally, a route is chosen according to an appropriate strategy (Section 3.3). 3.1. Route discovery Route discovery consists of two sub-procedures: Route Request (RREQ) and Route Replay (RREP). When a node joins the network, it does not know any paths to reach other nodes. In order to discover new routes, a participant uses a special packet called Route Request (RREQ), which includes its own identification, the destination required and the ID-request.
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Upon receiving that packet, a node can proceed in two different ways: 1. If it knows how to reach the destination, it will take the path from its route cache table and send back the whole path using a Route Replay (RREP) packet. 2. Otherwise, it forwards to its neighbours the packet generated by the source, appending its own address to the route record. These two situations are managed by each node that receives a RREQ. Finally, when that packet arrives at the destination node, the destination is able to read which nodes forwarded that request of the source. At this point the path is discovered, and is sent back to the source using a RREP. Fig. 1 shows how the RREQ is sent by source S through intermediate nodes A and B until it reaches destination D. In particular, the ID number is used to avoid forwarding a RREQ multiple times, generating an infinite loop. The RREP contains the path built from the source to the destination; that path is sent back to the source using the very nodes that forwarded the previous RREQ. 3.2. Route maintenance Mobility is an important aspect of MANETs. Links between nodes may change, and in consequence the paths known by a node may have to be updated. DSR uses the Route Maintenance procedure, which is exemplified in Fig. 2 between source S and destination D with two intermediary nodes A and B. Every node is responsible to check if its link with the next one is still alive. For example, in the scenario of Fig. 2, node A checks for the link A–B and node B for the link B–D. If A detects a problem with its link to B, it waits for a prescribed number of times for the acknowledgement message from B. If that message fails to arrive, A removes the link A–B from its cache route table. Finally, it spreads the news of the missing link to the source node using a Route Error (RERR) packet. 3.3. DSR and shortest path first After route discovery, a source node has collected different paths to reach destination and all traversed nodes. The source stores those paths locally in its cache route table. When it wants to transmit packets to a node, it searches its cache route table for possible paths to the destination. Thus, the sender will select a path using the Shortest Path First (SPF) strategy. Some of the limitations of this choice are discussed in [20]. In particular, it is shown that using SPF to select a path can lead to problems like congestion. To overcome this problem, it is suggested to use an emergency exit to redirect packets through an alternative route. We observe that DSR has other weaknesses. For example, Fig. 3 shows that different paths exist from source S to destination D. The SPF selection strategy may not always be optimal when considering the remaining battery power of the nodes on the shortest path. If communication between S and D is required persistently, DSR as its stands will stress out the intermediary nodes A and B till they run
Fig. 1. Path discovery with RREQ.
Fig. 2. Route maintenance with RERR.
out of battery: the links they were providing will therefore break down. 4. Dynamic path switching We set out to investigate a route maintenance protocol aimed at preserving each node alive as long as possible, more so as the node approaches the end of its battery life. Our main goal is to allow nodes to preserve battery during long – in terms of time – forwarding. We present a novel protocol that appreciates overloaded nodes and moves them to a sleep state if some conditions are respected. In addition, to keep existing connections alive, nodes are able to select alternative paths dynamically, though still avoiding sleeping nodes. On a real scenario, when a node acting as forwarder realises that it is consuming too much energy to honour its task, it would request to quit the forwarding state to save its remaining energy for other tasks. However, the node can quit forwarding only if others can take over its role. Otherwise its request will not be accepted. This is crucial because a node cannot decide arbitrarily to leave its role and create connectivity problems. 4.1. Conditions for going to sleep A forwarding node shall go to a sleep state either if: 3. it has forwarded packets for a long time, or if: 4. it is finishing its battery. Let N be a generic forwarding node. Let: • • • •
e(N) be N's total battery power (when fully charged); es(N) be N's battery level at the beginning of the forwarding session; ec(N) be N's current battery level; δ be the maximum percentage of es(N) that can be spent in forwarding; • γ be the minimum percentage of e(N) that must be preserved. Therefore, we can state more precisely that a node N shall go to a sleep state either if e ðNÞ 1− c 100 > δ es ðNÞ or if ec ðNÞ 100bγ: eðN Þ
Fig. 3. Example paths between source S and destination D.
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We shall see below that a typical simulation threshold will be δ ranging between 20% and 30%, and γ = 15%. 4.2. Sending a sleep request Let us focus on node B whose chain of forwarders includes a DPS-compliant node A. If B wants to change to a sleep state (because either of the two conditions stated above is met) it sends the following message:
5. Simulations The route maintenance strategy implemented above as a protocol can be embedded into reactive protocols for routing, such as DSR or AODV [21], preserving backward compatibility. We simulated such embedding within DSR using the Network Simulator 2 (NS2), which is the most established simulator in the area [22]. Our main goal is to simulate battery power consumption comparatively when nodes run DSR, hence are not power aware, and when nodes run DPS, hence are power aware.
1ÞB→A : May I sleep; xd : 5.1. Simulation environment In the message, the request to go to a sleep state is clear. In addition, xd denotes the destination node(s) of the path(s) in which the link A–B is involved. Obviously B must wait for feedback from A before changing state. 4.3. Handling a sleep request When the called node A receives message #1, it must check if alternative paths exist for each of the xd destinations. Now, if A finds out that alternative routes are available, it sends back to B a positive acknowledgement as follows: 2Y ÞA→B : Yes you can otherwise, it sends a negative feedback as: 2N ÞA→B : No you cannot:
In case of a positive reply, A should also remove all links towards B from its cache route table, and should then propagate this information to the other nodes. As a result, B goes to a sleep state for a predetermined, constant time, during which it will be skipped by traffic. During that time, B might for example switch off its wi-fi adapter to save energy. It is worth remarking that, from a logical standpoint, the sleep conditions stated in the previous section, which only pertain to the battery level of the calling node, have lower priority than the enforcement of network connectivity. In particular, if B meets either condition, it may still be the case that the called node A finds no alternative path. In that case, B will still get a negative reply until the topology of the network changes eventually, with some other node getting closer to replace B as a forwarder. This differs, for example, from [11], where a node could go to sleep abruptly and hinder network connectivity. Fig. 4 portrays the protocol described above, with message #2 taking form of either #2Y or #2N. Fig. 5 shows a possible network topology resulting from node B's being in a sleep state. The new path S–A–E–F–D from source to destination can be seen to include nodes with good battery levels. Selection of this path was entrusted to the routing protocol but this time B was excluded. It is now clear how DPS trades path length for battery power totalled by nodes on a path.
Fig. 4. A sleep request.
We adopted the energy model provided by NS2, which allowed us to set the fundamental parameters required for our tests. In particular, we set 200 J as initial battery energy for each node, 1.5 W as power required for transmission of a Link Layer frame, and 1.0 W as power consumed to receive a frame. Other minor quantities were set (here omitted) as battery power necessary for example for a node to change state from active to sleep and vice versa. The scenario used for simulations is shown in Fig. 6. We disposed 30 nodes with a particular layout, whereas the communication range of each node is 200 m. The motivation for this choice is clear. This is the most stressing layout for power-aware routing protocols because connectivity is maximised by having each node with the same number of neighbours. As a result, the layout features multiple alternative paths of equal length to go from a source to a destination, so that power awareness can be evaluated. Further, we decided to use static nodes to get accurate power consumption findings about nodes that DSR would stress because they live in a shortest path. Most importantly, this choice is due to facilitating a comparison of DPS with DSR, which is our main goal, because no extra variables depending on mobility must be considered. Despite this choice, we claim that our protocol properly works with mobile nodes. We recall that in our simulations, when a source node decides to send packets to a destination node, it creates a route discovery to find paths to the destination. When a path is chosen and communication starts, the source always uses CBR messages to the destination until communication ends. 5.2. DSR versus DPS As shown in Fig. 7, our simulations focus on a communication link between node 6, taken as source, and node 21, taken as destination. The simulation is set to last for 2000 s, during which always the same forwarders are selected: 13–19–20. This means that, after route discovery, DSR chose that very path. In particular, they started with the maximum amount of energy but during forwarding their energy decreases rapidly. As it was expected, the simulation confirmed that the forwarders spend a significant amount of battery power.
Fig. 5. Selection of a new path.
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Fig. 6. Network topology used for simulations.
Findings change when nodes implement DPS. Fig. 8 indicates that other nodes are called up to carry out packet forwarding between source and destination. More in detail, the simulation confirms that DPS still chooses one of the shortest paths first, indicated as path P1, because route discovery is unchanged with respect to DSR. When node 19 must go to sleep (because it meets one of the conditions stated above) (Section 4.1), the previous node in the chain, node 13 checks for alternative routes to the destination. But the error link reaches also the source, so that a completely different path is chosen, that is path P2, involving forwarders 7–14–15. With the power values set above, we observed that transmission over path P1 lasted for 850 s, while transmission over path P2 for only 440 s. This is due to our choice to set the battery level of the nodes on the second path to a lower level than those on the first path. The shorter-in-time transmission over the second path supports the claim that DPS is power aware. When node 14
must go to sleep, path P3 is selected, involving forwarders 7–8–15, and is used for 470 s. Finally, route P4 is selected, and it is the same as P1. It is used until the transmission terminates. Our simulations show that DPS involves several nodes in the forwarding task. In particular, it involves more than those that DSR uses (7 against 3). This confirms that the total consumption of battery power is somewhat distributed among a number of nodes. 5.3. Energy balancing The results described above can be interpreted in terms of overall battery levels of the nodes after packet transmission. Fig. 9 offers a graphical representation of the remaining battery levels of nodes after packet transmission from S to D using DPS or DSR. The vertical lines depict the battery levels: in particular, the left lines refer to DPS and the right ones to DSR. It can be seen that
Fig. 7. Path selection using DSR.
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Fig. 8. Path maintenance using DPS.
the destination node has a rather good battery level in both cases; this is due to the fact that it only participated by receiving packets, which is generally inexpensive. By contrast, considering the DSR case, forwarders consumed significantly more power, as the brown lines indicate. Also those nodes not actively involved in the communication consumed some energy, which is due to their remaining ON and willing to participate. Conversely, it can be appreciated that DPS changes the general balance of the battery levels because it involves a larger number of forwarders. Precisely, it allows a wider distribution of energy consumption. In addition, nodes that DSR stressed by packet forwarding, can now take advantage of DPS by asking others to accomplish their tasks.
180 170 160 Energy 150 140 130 120 110 100 200
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Fig. 9. Energy consumption using DPS or DSR.
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Time (s) Fig. 10. Energy consumption of forwarder using DSR or DPS.
5.4. Focusing on a single forwarder It is useful to focus on energy consumption of a single forwarder who adopts DSR or DPS. Fig. 10 assists the comparison. The green line traces the trend of an observed forwarder that uses DSR, while the red line regards the same forwarder when using DPS. It can be observed that the two lines overlap for about 1000 s of simulation. Then, the red line changes slope because the forwarder switches to a sleep state, and the path from source to destination must change. 6. Conclusions Overall, the DSR strategy, of choosing always the shortest route to send packets from a source to a destination, does not always give the best results in terms of power awareness. It can be argued that the route maintenance protocol in a MANET should help a node preserve battery energy. By contrast, our simulations confirm that this is not always the case when using DSR. The battery energy of forwarding nodes can be easily depleted. To make the routing protocol power aware, fresh information about energy levels should be gathered dynamically from all nodes. A number of protocols, reviewed above, do this to the detriment of backward compatibility. We have taken an orthogonal approach, of allowing for power awareness at route maintenance time, and developed a new protocol termed Dynamic Path Switching (DPS). A node that is in a critical energy condition may go to sleep if at least one in its chain of forwarders is DPS-complaint and can preserve network connectivity. This preserves backward compatibility with DSR. We upgraded DSR with the new route maintenance protocol and tested both the original DSR and the upgraded one in the simulation environment NS2. Extensive simulations confirm a much increased power awareness of the upgraded protocol. References [1] A. Inc., Apple Inc.-, http://www.apple.com/iphone/ 2010. [2] N. Balasubramanian, A. Balasubramanian, A. Venkataramani, Energy consumption in mobile phones: a measurement study and implications for network applications, In: IMC '09: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, ACM, New York, NY, USA, 2009, pp. 280–293. [3] D. Johnson, Y. Hu, D. Maltz, The Dynamic Source Routing protocol (DSR) for Mobile Ad Hoc Networks for IPv4, In: Internet RFC 4728, 2007, p. 107. [4] S. Mccanne, S. Floyd, K. Fall, The Network Simulator 2, 2009. [5] Y.C. Tseng, C.S. Hsu, T.Y. Hsieh, Power-saving protocols for IEEE 802.11-based multi-hop ad hoc networks, Computer Networks: The International Journal of Computer and Telecommunications Networking 43 (2003) 317–337.
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[22] S. Kurkowski, T. Camp, M. Colagrosso, Manet simulation studies: the incredibles, ACM SIGMOBILE Mobile Computing and Communications Review 9 (2005) 50–61. Giampaolo Bella does teaching and research in Computer Science with the University of Catania, Royal Holloway University of London and De Montfort University. His main research interests lie in the analysis of crucial security properties by formal methods based on mechanical tools such as proof assistants, or manual tools such as soft constraint solving. After his Ph.D. from Cambridge University Computer Laboratory, he was a research associate at Technical University Munich for a semester, and then back at Cambridge University for a three-year EPSRC project on verifying e-commerce protocols. In 2008, he was a senior researcher with SAP Research France on the European Project AVANTSSAR for the validation of service-oriented architectures. He has published the book "Formal Correctness of Security Protocols" with Springer-Verlag in the Information Security and Cryptography series (ISBN: 978-3-540-68134-2), where he investigates proofs of correctness of realistic security protocols using a formal language with strong machine support. Bella acts as PC member for various international conferences in the area of formal methods or computer security. He has chaired the track in Computer Security at the ACM Symposium on Applied Computing since its inception in 2002.
With a thesis entitled "Enforcing trust, collaboration and power-saving in MANETs", Gianpiero Costantino received his Ph.D. degree from the University of Catania (Italy) in 2011. This followed his Master and Bachelor degrees from the same University, respectively in 2007 and 2005. He currently is a post-doc researcher at the Institute of Telematics and Informatics (IIT) of the National Research Council (CNR) located in Pisa (Italy). He is involved in two main research projects: mechanisms of trust management for Web Services, and preservation of users' privacy within Opportunist Networks.
Jon Crowcroft is the Marconi Professor of Networked Systems in the Computer Laboratory, of the University of Cambridge. Prior to that he was professor of networked systems at UCL in the Computer Science Department. He is a Fellow of the ACM, a Fellow of the British Computer Society and a Fellow of the IEE and a Fellow of the Royal Academy of Engineering, as well as a Fellow of the IEEE. He was a member of the IAB 96-02, and went to the first 50 IETF meetings; was general chair for the ACM SIGCOMM 95-99; is recipient of Sigcomm Award in 2009. He is the Principle Investigator in the Computer Lab for the EU Social Networks project, the EPSRC funded Horizon Digital Economy project, hubbed at Nottingham, the EPSRC funded project on federated sensor nets project FRESNEL, in collaboration with Oxford; and a new 5-year project towards a Carbon Neutral Internet with Leeds.
Salvatore Riccobene was born in Catania, in 1967. He obtained the "Laurea" degree in Electronic Engineering from the University of Catania in 1992 and the PhD in Computer Science Engineering from the University of Palermo in 1996. Since 1996 he is with the Department of Mathematics and Computer Science of the University of Catania, first as Assistant professor and, from 2001, as Associate Professor of Computer Science. His main research area include parallel and distributed systems, wireless network, protocol design in cooperative environment.
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Computer Standards & Interfaces journal homepage: www.elsevier.com/locate/csi
Enhancing DSR maintenance with power awareness Giampaolo Bella a,⁎, Gianpiero Costantino b, Jon Crowcroft c, Salvatore Riccobene a a b c
Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy IIT-CNR, Pisa, Italy Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
a r t i c l e
i n f o
Article history: Received 15 September 2011 Received in revised form 25 February 2012 Accepted 28 June 2012 Available online 14 July 2012 Keywords: Power saving Wireless network Mobile device Collaboration
a b s t r a c t A power-aware route maintenance protocol for Mobile Ad Hoc Networks (MANETs) is introduced. Termed Dynamic Path Switching (DPS), the new protocol puts an overloaded node to sleep before a route link breaks because that node runs out of energy, and brings other suitable nodes into play instead. When the battery charge of a node reaches a stated level, the node can advance a request to change to a sleep state for a while. The request is honoured unless survival of some path rests on the forwarding activity of that very node. All nodes are assumed to be collaborative. The DPS protocol is fully backward compatible, as it can be implemented within existing routing protocols such as Dynamic Source Routing (DSR). The new protocol has been extensively simulated with the established network simulator NS2. The findings indicate a much improved power awareness of the updated routing protocol with respect to the unadorned one. Power saving is particularly effective during long-lived sessions. © 2012 Elsevier B.V. All rights reserved.
1. Introduction If we lived in a world where energy was infinite, we would not have to worry about battery-life in our mobile devices. Unfortunately, reality is different and, for example, the smartphone industry finds battery life a seriously limiting constraint. In particular, the number and range of applications requiring a lot of battery power is increasing rapidly. While research to increase battery life is making progress, mobile device makers are investigating a number of techniques to preserve battery power. Just a few years back, it was not rare to enforce computational limitations exactly to save battery power, as with the first generation of iPhones [1], which only came with EDGE connectivity rather than 3G. EDGE is slower than 3G but uses less battery power, as shown by Balasubramanian et al. [2]. Although it remains uncertain whether power saving was the real motivation for such an important technological hence commercial choice, Apple publicly claimed 3G hardware to be hungry of battery power. It was only a year later when Apple introduced iPhone 3G, though still continuing to thwart battery consumption by enforcing computational limitations. It may seem emblematic that multitasking did not arrive until the fourth version of their OS. It is clear that the battery power of mobile nodes is a limited resource, and there is no way to allow a device to live forever, hence techniques to maximise battery life are relevant. Our research focuses ⁎ Corresponding author. E-mail addresses: [email protected] (G. Bella), [email protected] (G. Costantino), [email protected] (J. Crowcroft), [email protected] (S. Riccobene). 0920-5489/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.csi.2012.06.007
on power saving within Mobile Ad Hoc Networks (MANETs). Here, only mobile devices, such as smartphones or PDAs, can take part. No infrastructure is provided, and when a new node (also addressed as device below) joins the MANET, it is ready to communicate both with and on behalf of other nodes. Message forwarding is in fact carried out by participants in the network and no dedicated routers are required. Mobility may also influence the role that nodes take in a network. However, because mobility is usually within a bounded space, the selected path between two end nodes is likely to remain unvaried. Traffic between the two end nodes will then involve a constant set of intermediary nodes acting as packet forwarders. For example, a device in a crowded location may have its resources called upon frequently, if the routing protocol chooses it for long time. The forwarders' persistent activity dramatically consumes their batteries, increasing the risks of flattening them. Although routes are often built on demand in order to get the most reliable path at a given moment, the path selected may not be best in terms of remaining battery power of the devices that form it. Hence, nodes in a crucial network position may deplete their battery rapidly because they may have to forward vast traffic. This is a severe limitation. We introduce the Dynamic Path Switching (DPS) route maintenance protocol to allow the nodes that are stressed persistently, and hence strain their batteries, to call for the selection of a new route. DPS is therefore power-aware. In particular, when a node has been forwarding packets for a long time, it may request to quit its role in order to preserve battery. If it is determined that the right conditions are respected, then the device will suspend its activity. Hence, a node will less likely run out of battery. It is assumed that all nodes comply with the routing protocol, hence no node will suspend its activity
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arbitrarily — no node will take a selfish behaviour and save battery improperly. We modified the Dynamic Source Routing (DSR) [3] protocol extending it with DPS. The new routing protocol was evaluated using the Network Simulator 2 (NS2) [4] through extensive simulations. The findings will be comparatively presented below over the power-aware DPS and the power-unaware DSR, confirming the benefits of the former, especially in extensive communications. The presentation begins with a survey of other recent research related to power saving (Section 2). Then, it provides an overview of the key features of the Dynamic Source Routing protocol (Section 3). After that, it introduces our routing protocol (Section 4), and our simulation results (Section 5). The paper terminates with conclusions and discussions of potential future work (Section 6). 2. Related work Researchers are currently investigating several areas related to power saving. Tseng et al. list them as transmission power control, power-aware routing and low-power mode [5]. The first area features research efforts towards avoiding collision or minimising battery consumption during transmission. There are various procedures to allow nodes to save battery while keeping the network connected [6,7]. Even during packet transmission, each node can balance the transmission strength to its battery level. Using the same approach of tuning the battery consumption during transmission, Wang proposes a technique to calculate the precise level of battery power to spend in communications [8]. Nodes compute it during the route discovery process run by a source. When a route request is sent along a path, the setup packet stores the relevant information. This is then used to compute the power needed to forward a packet hop-by-hop. The procedure is repeated by all nodes that forward the request. Finally, path setup is reflected back by the receiver, carrying all the information gathered in the forward path before. From now on, the source can choose the path where the total residual battery power of nodes is at its maximum. Power-aware routing is the second area of research in power saving. Kim and Koo advance a power-aware routing [9]. Their protocol modifies the original link between nodes reducing the energy consumption. By allowing the use of more hops, if necessary, a node can send a packet to a neighbour closer to the destination, reducing the power required for forwarding. PARO and ZRP name the two procedures that implement this approach. Tao and Luo introduce an extension to DSR to make the protocol power-aware in [10]. By using all routes found using DSR, they introduce a function called dynamic priority-weight, which is used to compare in terms of required battery power all paths that are found. The one that requires least power will be the candidate for end-to-end communication. Maleki et al. in [11] advance a new approach to increase nodes' lifetime by means of min-cost routing. They modify the protocol for DSR-Route Discovery and DSR-Route Maintenance as follows: a destination node, which receives different paths from the RREQ sent by a sender, is able to select the route that costs less. However, a node that is involved in heavy-forwarding may opt for disconnection to preserve its energy. This will break all paths that traverse it, raising the risk of network partitioning. Also, the protocol is not backward compatible because all participating nodes must conform to it. The authors of [12] propose a combination of two strategies to reduce the battery consumption of nodes. They consider both the residual energy of a node and the current request of energy due to the tasks of the node. The latter analysis is done by monitoring the activity of the node, such as the number of packets in its queue. By means of this study, it is possible to have an estimation of the nodes' lifetime, and they can be discarded when their energy is insufficient. A comparative analysis of different energy-aware protocols was proposed by Safwat et al. in [13]. They implemented a simulator that studies the energy consumption from
the physical and MAC layer. The routing-aware protocols compared by the authors are: minimum battery cost routing (MBCR) [14], min– max battery cost routing (MMBCR) [14], maximum battery life routing (MTPR) [15], and, finally, the combination of the last two protocols (CMMBCR). Findings show how these protocols gave different results when the network's mobility changes. A comparative study of the aforementioned protocols was done by Cano and Kim in [16]. Also, a survey of existing energy-aware protocols is illustrated in [17]. It organises the existing literature into different categories: active and passive energy savings, network lifetime maximising protocols, topology control protocols, energy-efficient multicasting and broadcasting protocols. Each work is explained in depth, while advantages and disadvantages are highlighted. Two works are most representative of the low-power mode area. Wang et al. develop another protocol to preserve battery in [18]. They make use of global synchronisation [19] to coordinate beacon intervals between devices. This is done by using a pre-chosen hash function with the MAC-address of the receiver. In fact, that unique value is used as input for the hash function and the result makes it easier to understand the listening period of a neighbour. This process is reiterated until the packet arrives at the final node. Another solution based on different states of a node is due to Tseng et al. [5]. They implement three distinct protocols to keep a node in power-saving mode as long as possible though still maintaining network connectivity. The focus of the present paper can be seen as lying between low-power mode and power-aware routing. The main requirement is to maximise backward compatibility with the original DSR protocol. Hence, the original route discovery protocol of DSR, contrarily to previous work such as [11], is kept unmodified. The goal of this paper is an extended maintenance protocol aimed at dynamically favouring survival of nodes that are in a critical energy condition. The new maintenance protocol allows each node that is low on energy to seek permission to sleep from anyone in its chain of forwarders, while preserving connectivity. The only requirement on the called node is DPS compliance. Remarkably, for this protocol to work, it is sufficient that at least one among the forwarders is DPS-compliant. Our contribution is more geared towards individual survival than towards overall power consumption throughout a route. This is motivated by scenarios where nodes are collaborative but do not want to die. Intermediate scenarios appear to be worthy of further investigation. 3. Dynamic source routing Routing protocols of MANETs fall into two main families. The first are proactive protocols, which use a table-driven approach to establish paths, hence the reliability of routing paths depends on table refreshing. If this is done too often, control traffic can overload connection links. On the contrary, reactive protocols build paths on-demand and Dynamic Source Routing (DSR) is among the best known. We adopt it here as our running example, and outline it briefly below, while for a complete description we refer the reader to the original paper [3]. As other routing protocol, DSR employs flooding to discover paths (Section 3.1). Then, to manage situations in which current routes are broken, DSR implements a separate route maintenance procedure (Section 3.2). Finally, a route is chosen according to an appropriate strategy (Section 3.3). 3.1. Route discovery Route discovery consists of two sub-procedures: Route Request (RREQ) and Route Replay (RREP). When a node joins the network, it does not know any paths to reach other nodes. In order to discover new routes, a participant uses a special packet called Route Request (RREQ), which includes its own identification, the destination required and the ID-request.
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Upon receiving that packet, a node can proceed in two different ways: 1. If it knows how to reach the destination, it will take the path from its route cache table and send back the whole path using a Route Replay (RREP) packet. 2. Otherwise, it forwards to its neighbours the packet generated by the source, appending its own address to the route record. These two situations are managed by each node that receives a RREQ. Finally, when that packet arrives at the destination node, the destination is able to read which nodes forwarded that request of the source. At this point the path is discovered, and is sent back to the source using a RREP. Fig. 1 shows how the RREQ is sent by source S through intermediate nodes A and B until it reaches destination D. In particular, the ID number is used to avoid forwarding a RREQ multiple times, generating an infinite loop. The RREP contains the path built from the source to the destination; that path is sent back to the source using the very nodes that forwarded the previous RREQ. 3.2. Route maintenance Mobility is an important aspect of MANETs. Links between nodes may change, and in consequence the paths known by a node may have to be updated. DSR uses the Route Maintenance procedure, which is exemplified in Fig. 2 between source S and destination D with two intermediary nodes A and B. Every node is responsible to check if its link with the next one is still alive. For example, in the scenario of Fig. 2, node A checks for the link A–B and node B for the link B–D. If A detects a problem with its link to B, it waits for a prescribed number of times for the acknowledgement message from B. If that message fails to arrive, A removes the link A–B from its cache route table. Finally, it spreads the news of the missing link to the source node using a Route Error (RERR) packet. 3.3. DSR and shortest path first After route discovery, a source node has collected different paths to reach destination and all traversed nodes. The source stores those paths locally in its cache route table. When it wants to transmit packets to a node, it searches its cache route table for possible paths to the destination. Thus, the sender will select a path using the Shortest Path First (SPF) strategy. Some of the limitations of this choice are discussed in [20]. In particular, it is shown that using SPF to select a path can lead to problems like congestion. To overcome this problem, it is suggested to use an emergency exit to redirect packets through an alternative route. We observe that DSR has other weaknesses. For example, Fig. 3 shows that different paths exist from source S to destination D. The SPF selection strategy may not always be optimal when considering the remaining battery power of the nodes on the shortest path. If communication between S and D is required persistently, DSR as its stands will stress out the intermediary nodes A and B till they run
Fig. 1. Path discovery with RREQ.
Fig. 2. Route maintenance with RERR.
out of battery: the links they were providing will therefore break down. 4. Dynamic path switching We set out to investigate a route maintenance protocol aimed at preserving each node alive as long as possible, more so as the node approaches the end of its battery life. Our main goal is to allow nodes to preserve battery during long – in terms of time – forwarding. We present a novel protocol that appreciates overloaded nodes and moves them to a sleep state if some conditions are respected. In addition, to keep existing connections alive, nodes are able to select alternative paths dynamically, though still avoiding sleeping nodes. On a real scenario, when a node acting as forwarder realises that it is consuming too much energy to honour its task, it would request to quit the forwarding state to save its remaining energy for other tasks. However, the node can quit forwarding only if others can take over its role. Otherwise its request will not be accepted. This is crucial because a node cannot decide arbitrarily to leave its role and create connectivity problems. 4.1. Conditions for going to sleep A forwarding node shall go to a sleep state either if: 3. it has forwarded packets for a long time, or if: 4. it is finishing its battery. Let N be a generic forwarding node. Let: • • • •
e(N) be N's total battery power (when fully charged); es(N) be N's battery level at the beginning of the forwarding session; ec(N) be N's current battery level; δ be the maximum percentage of es(N) that can be spent in forwarding; • γ be the minimum percentage of e(N) that must be preserved. Therefore, we can state more precisely that a node N shall go to a sleep state either if e ðNÞ 1− c 100 > δ es ðNÞ or if ec ðNÞ 100bγ: eðN Þ
Fig. 3. Example paths between source S and destination D.
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We shall see below that a typical simulation threshold will be δ ranging between 20% and 30%, and γ = 15%. 4.2. Sending a sleep request Let us focus on node B whose chain of forwarders includes a DPS-compliant node A. If B wants to change to a sleep state (because either of the two conditions stated above is met) it sends the following message:
5. Simulations The route maintenance strategy implemented above as a protocol can be embedded into reactive protocols for routing, such as DSR or AODV [21], preserving backward compatibility. We simulated such embedding within DSR using the Network Simulator 2 (NS2), which is the most established simulator in the area [22]. Our main goal is to simulate battery power consumption comparatively when nodes run DSR, hence are not power aware, and when nodes run DPS, hence are power aware.
1ÞB→A : May I sleep; xd : 5.1. Simulation environment In the message, the request to go to a sleep state is clear. In addition, xd denotes the destination node(s) of the path(s) in which the link A–B is involved. Obviously B must wait for feedback from A before changing state. 4.3. Handling a sleep request When the called node A receives message #1, it must check if alternative paths exist for each of the xd destinations. Now, if A finds out that alternative routes are available, it sends back to B a positive acknowledgement as follows: 2Y ÞA→B : Yes you can otherwise, it sends a negative feedback as: 2N ÞA→B : No you cannot:
In case of a positive reply, A should also remove all links towards B from its cache route table, and should then propagate this information to the other nodes. As a result, B goes to a sleep state for a predetermined, constant time, during which it will be skipped by traffic. During that time, B might for example switch off its wi-fi adapter to save energy. It is worth remarking that, from a logical standpoint, the sleep conditions stated in the previous section, which only pertain to the battery level of the calling node, have lower priority than the enforcement of network connectivity. In particular, if B meets either condition, it may still be the case that the called node A finds no alternative path. In that case, B will still get a negative reply until the topology of the network changes eventually, with some other node getting closer to replace B as a forwarder. This differs, for example, from [11], where a node could go to sleep abruptly and hinder network connectivity. Fig. 4 portrays the protocol described above, with message #2 taking form of either #2Y or #2N. Fig. 5 shows a possible network topology resulting from node B's being in a sleep state. The new path S–A–E–F–D from source to destination can be seen to include nodes with good battery levels. Selection of this path was entrusted to the routing protocol but this time B was excluded. It is now clear how DPS trades path length for battery power totalled by nodes on a path.
Fig. 4. A sleep request.
We adopted the energy model provided by NS2, which allowed us to set the fundamental parameters required for our tests. In particular, we set 200 J as initial battery energy for each node, 1.5 W as power required for transmission of a Link Layer frame, and 1.0 W as power consumed to receive a frame. Other minor quantities were set (here omitted) as battery power necessary for example for a node to change state from active to sleep and vice versa. The scenario used for simulations is shown in Fig. 6. We disposed 30 nodes with a particular layout, whereas the communication range of each node is 200 m. The motivation for this choice is clear. This is the most stressing layout for power-aware routing protocols because connectivity is maximised by having each node with the same number of neighbours. As a result, the layout features multiple alternative paths of equal length to go from a source to a destination, so that power awareness can be evaluated. Further, we decided to use static nodes to get accurate power consumption findings about nodes that DSR would stress because they live in a shortest path. Most importantly, this choice is due to facilitating a comparison of DPS with DSR, which is our main goal, because no extra variables depending on mobility must be considered. Despite this choice, we claim that our protocol properly works with mobile nodes. We recall that in our simulations, when a source node decides to send packets to a destination node, it creates a route discovery to find paths to the destination. When a path is chosen and communication starts, the source always uses CBR messages to the destination until communication ends. 5.2. DSR versus DPS As shown in Fig. 7, our simulations focus on a communication link between node 6, taken as source, and node 21, taken as destination. The simulation is set to last for 2000 s, during which always the same forwarders are selected: 13–19–20. This means that, after route discovery, DSR chose that very path. In particular, they started with the maximum amount of energy but during forwarding their energy decreases rapidly. As it was expected, the simulation confirmed that the forwarders spend a significant amount of battery power.
Fig. 5. Selection of a new path.
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Fig. 6. Network topology used for simulations.
Findings change when nodes implement DPS. Fig. 8 indicates that other nodes are called up to carry out packet forwarding between source and destination. More in detail, the simulation confirms that DPS still chooses one of the shortest paths first, indicated as path P1, because route discovery is unchanged with respect to DSR. When node 19 must go to sleep (because it meets one of the conditions stated above) (Section 4.1), the previous node in the chain, node 13 checks for alternative routes to the destination. But the error link reaches also the source, so that a completely different path is chosen, that is path P2, involving forwarders 7–14–15. With the power values set above, we observed that transmission over path P1 lasted for 850 s, while transmission over path P2 for only 440 s. This is due to our choice to set the battery level of the nodes on the second path to a lower level than those on the first path. The shorter-in-time transmission over the second path supports the claim that DPS is power aware. When node 14
must go to sleep, path P3 is selected, involving forwarders 7–8–15, and is used for 470 s. Finally, route P4 is selected, and it is the same as P1. It is used until the transmission terminates. Our simulations show that DPS involves several nodes in the forwarding task. In particular, it involves more than those that DSR uses (7 against 3). This confirms that the total consumption of battery power is somewhat distributed among a number of nodes. 5.3. Energy balancing The results described above can be interpreted in terms of overall battery levels of the nodes after packet transmission. Fig. 9 offers a graphical representation of the remaining battery levels of nodes after packet transmission from S to D using DPS or DSR. The vertical lines depict the battery levels: in particular, the left lines refer to DPS and the right ones to DSR. It can be seen that
Fig. 7. Path selection using DSR.
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Fig. 8. Path maintenance using DPS.
the destination node has a rather good battery level in both cases; this is due to the fact that it only participated by receiving packets, which is generally inexpensive. By contrast, considering the DSR case, forwarders consumed significantly more power, as the brown lines indicate. Also those nodes not actively involved in the communication consumed some energy, which is due to their remaining ON and willing to participate. Conversely, it can be appreciated that DPS changes the general balance of the battery levels because it involves a larger number of forwarders. Precisely, it allows a wider distribution of energy consumption. In addition, nodes that DSR stressed by packet forwarding, can now take advantage of DPS by asking others to accomplish their tasks.
180 170 160 Energy 150 140 130 120 110 100 200
170 160 150 140 130 120 110 800
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Fig. 9. Energy consumption using DPS or DSR.
Remaining Energy (J)
180 DPS DSR
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110 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600
Time (s) Fig. 10. Energy consumption of forwarder using DSR or DPS.
5.4. Focusing on a single forwarder It is useful to focus on energy consumption of a single forwarder who adopts DSR or DPS. Fig. 10 assists the comparison. The green line traces the trend of an observed forwarder that uses DSR, while the red line regards the same forwarder when using DPS. It can be observed that the two lines overlap for about 1000 s of simulation. Then, the red line changes slope because the forwarder switches to a sleep state, and the path from source to destination must change. 6. Conclusions Overall, the DSR strategy, of choosing always the shortest route to send packets from a source to a destination, does not always give the best results in terms of power awareness. It can be argued that the route maintenance protocol in a MANET should help a node preserve battery energy. By contrast, our simulations confirm that this is not always the case when using DSR. The battery energy of forwarding nodes can be easily depleted. To make the routing protocol power aware, fresh information about energy levels should be gathered dynamically from all nodes. A number of protocols, reviewed above, do this to the detriment of backward compatibility. We have taken an orthogonal approach, of allowing for power awareness at route maintenance time, and developed a new protocol termed Dynamic Path Switching (DPS). A node that is in a critical energy condition may go to sleep if at least one in its chain of forwarders is DPS-complaint and can preserve network connectivity. This preserves backward compatibility with DSR. We upgraded DSR with the new route maintenance protocol and tested both the original DSR and the upgraded one in the simulation environment NS2. Extensive simulations confirm a much increased power awareness of the upgraded protocol. References [1] A. Inc., Apple Inc.-, http://www.apple.com/iphone/ 2010. [2] N. Balasubramanian, A. Balasubramanian, A. Venkataramani, Energy consumption in mobile phones: a measurement study and implications for network applications, In: IMC '09: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, ACM, New York, NY, USA, 2009, pp. 280–293. [3] D. Johnson, Y. Hu, D. Maltz, The Dynamic Source Routing protocol (DSR) for Mobile Ad Hoc Networks for IPv4, In: Internet RFC 4728, 2007, p. 107. [4] S. Mccanne, S. Floyd, K. Fall, The Network Simulator 2, 2009. [5] Y.C. Tseng, C.S. Hsu, T.Y. Hsieh, Power-saving protocols for IEEE 802.11-based multi-hop ad hoc networks, Computer Networks: The International Journal of Computer and Telecommunications Networking 43 (2003) 317–337.
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[22] S. Kurkowski, T. Camp, M. Colagrosso, Manet simulation studies: the incredibles, ACM SIGMOBILE Mobile Computing and Communications Review 9 (2005) 50–61. Giampaolo Bella does teaching and research in Computer Science with the University of Catania, Royal Holloway University of London and De Montfort University. His main research interests lie in the analysis of crucial security properties by formal methods based on mechanical tools such as proof assistants, or manual tools such as soft constraint solving. After his Ph.D. from Cambridge University Computer Laboratory, he was a research associate at Technical University Munich for a semester, and then back at Cambridge University for a three-year EPSRC project on verifying e-commerce protocols. In 2008, he was a senior researcher with SAP Research France on the European Project AVANTSSAR for the validation of service-oriented architectures. He has published the book "Formal Correctness of Security Protocols" with Springer-Verlag in the Information Security and Cryptography series (ISBN: 978-3-540-68134-2), where he investigates proofs of correctness of realistic security protocols using a formal language with strong machine support. Bella acts as PC member for various international conferences in the area of formal methods or computer security. He has chaired the track in Computer Security at the ACM Symposium on Applied Computing since its inception in 2002.
With a thesis entitled "Enforcing trust, collaboration and power-saving in MANETs", Gianpiero Costantino received his Ph.D. degree from the University of Catania (Italy) in 2011. This followed his Master and Bachelor degrees from the same University, respectively in 2007 and 2005. He currently is a post-doc researcher at the Institute of Telematics and Informatics (IIT) of the National Research Council (CNR) located in Pisa (Italy). He is involved in two main research projects: mechanisms of trust management for Web Services, and preservation of users' privacy within Opportunist Networks.
Jon Crowcroft is the Marconi Professor of Networked Systems in the Computer Laboratory, of the University of Cambridge. Prior to that he was professor of networked systems at UCL in the Computer Science Department. He is a Fellow of the ACM, a Fellow of the British Computer Society and a Fellow of the IEE and a Fellow of the Royal Academy of Engineering, as well as a Fellow of the IEEE. He was a member of the IAB 96-02, and went to the first 50 IETF meetings; was general chair for the ACM SIGCOMM 95-99; is recipient of Sigcomm Award in 2009. He is the Principle Investigator in the Computer Lab for the EU Social Networks project, the EPSRC funded Horizon Digital Economy project, hubbed at Nottingham, the EPSRC funded project on federated sensor nets project FRESNEL, in collaboration with Oxford; and a new 5-year project towards a Carbon Neutral Internet with Leeds.
Salvatore Riccobene was born in Catania, in 1967. He obtained the "Laurea" degree in Electronic Engineering from the University of Catania in 1992 and the PhD in Computer Science Engineering from the University of Palermo in 1996. Since 1996 he is with the Department of Mathematics and Computer Science of the University of Catania, first as Assistant professor and, from 2001, as Associate Professor of Computer Science. His main research area include parallel and distributed systems, wireless network, protocol design in cooperative environment.