technical reportSmart Sensor Networks are collections of non-mobile devices (S-elements) which can compute, communicate and sense the environment; they must be able to create local groups of devices (S-cliques). We propose here a protocol to solve the leadership problems for S-Nets. We sketch the correctness of this protocol in terms of an asynchronous network model, and give an example implementation

Henderson, Thomas C.

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The University of Utah: J. Willard Marriott Digital Library

Leadership Protocol for S-Nets Thomas C. Henderson UUCS-01-001 School of Computing University of Utah Salt Lake City, UT 84112 USA 24 February 2001 Abstract Smart Sensor Networks are collections of non-mobile devices (S-elements) which can com-pute, communicate and sense the environment; they must be able to create local groups of devices (S-cliques). We propose here a protocol to solve the leadership problem for S-Nets. We sketch the correctness of this protocol in terms of an asynchronous network model, and give an example implementation. Leadership Protocol for S-Nets Thomas C. Henderson School of Computing University of Utah Salt Lake City, UT, 84112 USA 22 February 2001 Abstract Smart Sensor Networks are collections ofnon-mobile devices (S-elements) which can compute, communicate and sense the environment; they must be able to create local groups of devices (S-cliques). We propose here a protocol to solve the leadership problem for S-Nets. We sketch the correctness of this protocol in terms of an asynchronous network model. 1. Introduction At one extreme, mobile robots can be provided with a wealth of on-board sensing, communication and computa-tional resources [1, 2]; at the other extreme, robots with fewer on-board resources can perform their tasks in the context of a large number of stationary devices distributed throughout the task environment [3]. We call the latter approach the Smart Sensor Network, or the S-Net. We have performed simulation experiments using software (C and Matlab), and the performance of robot tasks with and without the presence of an S-Net (i.e., a set of distributed sensor devices) has been evaluated in terms of various measures. See [4, 5] for a more detailed account. This approach can be exploited widely and across several scales of application; e.g., fire fighting robots. If mobile robots are used to fight forest fires, there may be several hot spots to extinguish or control. If sensor devices can be distributed in the environment, then their values and gradients can be used to direct the behavior of fire fighting robots and to transport fire extinguishing materials from a depot to the closest fire source. During this movement to and from the fire, collision avoidance algorithms can be employed. Sometimes coordinated activities are necessary and communication models are also important. In our previous work, we provided models for various components of the study: (1) mobile robots with on-board sensors, (2) communication, (3) the S-Net (includes computation, sensing and communication), and (4) the simulation environment. We have developed algorithms in the simulation environment for the S-Net which perform cooperative computation and provide global information about the environment. Local and global frames are defined and created. A method for the production of global patterns using reaction-diffusion equations has been described and its relation to multi-robot cooperation demonstrated. In addition, we have shown how to compute shortest paths in the S-Net using level set techniques [7]. The results of our simulation experiments help us better understand the benefits and drawbacks of the S-Net. We have shown that for behaviors of one mobile robot going to a temperature source, and multiple mobile robots surrounding a temperature source, in the ideal situation (which means no noise), the S-Net takes more time and distance. But when noise is added in, which is more realistic, the S-Net system is more robust than the non-S-Net system. For the task of multiple mobile robots going back and forth to a temperature source, there are thresholds above which the S-Net system out-performs the non-S-Net system. 1 In this paper, we begin our conversion of the uni-processor S-Net simulation code to embedded processor algorithms. The set of necessary distributed algorithms includes: • S-Clique formation with leader • Coordinate frame calculation • Gradient calculation • Reaction-Diffusion computation • Shortest Paths using level sets. The first necessary algorithm is a solution to the leadership problem. The Leadership Problem: Each S-element has a unique integer ID (UID) and a fixed geographic location; S-elements have a restricted broadcast range which defines a connectivity graph. The S-elements are to be grouped into subgraphs, called S-cliques, such that each S-clique has a leader, and the leader of each S-clique has the lowest ID of all members of the S-clique. In this paper, we describe an algorithm to solve the S-clique leadership problem. For a good introduction to distributed algorithms, including solutions to variations of the leadership problem and correctness proofs, see [6]. 2. Leadership Protocol An S-net system will be represented as an undirected graph where each node is an S-element. The graph is undi-rected because if one S-element is within range to receive communication from another, then it works both ways. Each node can be viewed as a distinct process; in our implementation, they are Unix processes, but ultimately, they will be distinct hardware devices. Formal definitions can be given for the nodes, and this involves defining states, including start states, mes-sage generating functions, and state transitions. However, only an informal description is given here. Such a description will include broadcast() and receive() primitive functions with their associated messages. A broadcast sends a message to all S-elements within range. Proof methods typically involve either invariant assertions and a demonstration that they hold, or simulations. A simple example of a leadership algorithm is the LCR algorithm which provides a basic solution to the lead-ership problem in a synchronous ring network; it involves each process sending its UID to its neighbors; when a process receives an UID, it will throw it away if it is lesser than its own, resend it to its neighbors if it is larger than its own, and declare itself the leader if it is equal to its own. Our solution is related to this idea, although not the same. The S-Net leadership basic algorithm (SNL) is executed by each node, and is as follows: Algorithm SNL: Step 1. Broadcast own ID Step 2. Receive from other nodes, create neighbors list Step 3. Create remaining nodes list (initially, neighbors) while not done if node's own ID is lowest in remaining nodes list, 2 then node is leader broadcast cliques (self and neighbors) done else receive broadcast list if in list node is not a leader re-broadcast list done else remove list from remaining Note that we assume that enough time is given to steps 1 and 2 so that each node can complete the step correctly. This will most likely be implemented as a fixed time delay in an embedded system. Also, we assume that there are communications protocols that are reliable enough to transmit the messages without loss of information. 3. Correctness We outline an informal argument for the correctness of algorithm SNL. Let U = {I, 2, ... , uidmax }. The message alphabet M is the power set of U, i.e., P (U). The state of each node includes: • my_U I D( node i's unique UID (i.e., my_U I Di = i) • broadcast: a message in M or null, initially null • leader: a Boolean, indicating whether the node is a leader, intially false • resolved: a Boolean, indicating whether the node has resolved as either a leader or not, initially false Data structures used include: • neighbors: list of S-element neighbors, initially null • remaining: list of S-element neighbors still unresolved, initially null The start state for each node i is that initial set of values indicated above. For each node, the following messages are possible: • self: consists of my_U I Di • clique: list of UID's that form a clique The transition function for SNL is defined as: % Step 1 of SNL while (timerl > 0) broadcast self; endwhile % Step 2 of SNL while (timer2 > 0) 3 add_to_neighbors(receive) endwhile remaining = neighbors; % Step 3 of SNL while (not resolved) % Step 3.1 i f (my_UID(i) < min(remaining)) l e ader = true; res o lved = true; broadcast (my_UID(i) , remaining); endif list = receive; % Step 3.2 if (my_UID(i) in list) leader = false; resolved = true; broadcast (list) ; endif remaining = remaining - list; endwhile Note that the broadcast in (3 .2) has to take place so that a node i not in the clique, but neighboring a node j in the clique, can know that node j is resolved; this is necessary since the leader will not reach the non-clique nodes that neighbor clique nodes (i .e. , the broadcast from the leader node will not reach node i ). The algorithm is supposed to achieve: • (i) leader = t rue for any node that has the lowest UID of it and its unresolved neighbors. • (ii) leader = f alse for any node that neighbors a leader. • (iii) resolved = t rue for every node. Case (i) Suppose that node i has the lowest UID of it and any of its neighbors. Then when it finishes Step (2), Thus, in Step (3) , Node i then asserts itself as a leader. Case (ii) Suppose node i has a neighbor which eventually asserts itself a leader, say neiim _U I D . Then , rem aining = (neii l -U ID , .. . , neiim -U I D , . .. ) 4 and (3 .1) is always false as long as node i does not assert itself as a leader. This is true because neiim _U I D will not be removed from remaining unless an S-element is declared with node im as a member. Eventually, node im will assert itself as a leader, and will broadcast a list with node i as a member. Thus, (3 .2) will be true, and node i will declare itself not a leader. Case (iii) Every node is a leader or neighbors a leader. Thus, eventually one of cases (i) or (ii) will occur, and in each case, node i is resolved. 4. Implementation We have implemented this algorithm and tested it as a set of unique Unix processes that send and receive messages through files . The receive function is written so that a limited broadcast range is respected. As an example system, suppose that we have the following layout: 3 8 I I I I 2 -- -4 ---5---7 I I I I 1 6 \ with nodes located at: Node X-Location X-Location 1 1 1 2 1 2 3 1 3 4 2 2 5 3 2 6 4 1 7 4 2 8 4 3 We assume a broadcast range of 2. The neighbors found for the 8-node system is described in the trace as: Neighbors of Node 5 : 2 4 6 7 8 Neighbors of Node 3: 2 1 2 4 Neighbors of Node 4: 2 1 2 3 5 7 Neighbors of Node 8 : 2 5 6 7 Neighbors of Node 1: 2 2 3 4 Neighbors of Node 6 : 2 5 7 8 Neighbors of Node 2: 2 1 3 4 5 Neighbors of Node 7: 2 4 5 6 8 5 ". The resulting leaders and cliques are described as: Device 1 leader?: Yes Node 4 Clique 0: 1 2 3 4 Device 4 leader?: No Node 3 Clique 0: 1 2 3 4 Device 3 leader?: No Node 2 Clique 0: 1 2 3 4 Device 2 leader? : No Node 5 Clique 0: 1 2 3 4 Device 5 leader?: Yes Node 6 Clique 0: 5 6 7 8 Device 6 leader?: No Node 8 Clique 0: 5 6 7 8 Device 8 leader?: No Thus, the correct S-cliques were found, and the correct leaders were determined. 5. Summary and Conclusions We have developed a leadership protocol for S-Nets. This protocol has been demonstrated as a set of Unix pro-cesses communicating through files. The next set of issues include: • Develop a formal correctness proof for the algorithm. • Develop embedded system version of protocol. • Develop other distributed versions of the S-Net algorithms. • Implement complete set of S-device algorithms in physical testbed. References [1] Bares J E, Wettergreen D S 1999 Dante II: Technical description, results, and lessons learned. Int J Rob Res. 18(7):621-649 July [2] Smith R, Frost A, Probert 1999 P A Sensor System for the navigation of an underwater vehicle. Int J Rob Res. 18(7):697-710 July [3] Henderson, T C, Dckhil M, Morris S, Chen Y, Thompson W B 1998 Smart Sensor Snow. IEEE Con! IROS. Oct, pp 1377-1382 [4] Chen Y 2000 S-Nets: Smart Sensor Networks. MS Thesis, University of Utah [5] Henderson, T C, Chen Y, 2000 Smart Sensor Networks. IEEE Con! ISER. Dec, pp 85-94 [6] Lynch N 1996 Distributed Algorithms. Morgan Kaufman Pub, San Francisco [7] Sethian J A 1999 Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge UK 6 

Leadership protocol for S-Nets

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Leadership protocol for S-Nets

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